Background

This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.

Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.

The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.

The code in this module builds on code available in _v003, with function and mapping files updated:

Broadly, the CDC data analyzed by this module includes:

Functions and Mapping Files

The tidyverse package is loaded and functions are sourced:

# The tidyverse functions are routinely used without package::function format
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(geofacet)

# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")

A series of mapping files are also available to allow for parameterized processing. Mappings include:

These default parameters are maintained in a separate .R file and can be sourced:

source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")

Example Code Processing

The function is run to download and process the latest CDC case, hospitalization, and death data:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220220.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220220.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220220.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220206")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220206")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220206")$dfRaw$vax
                    )

cdc_daily_220220 <- readRunCDCDaily(thruLabel="Feb 18, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-01-30 new_deaths      796      539      257 0.38501873
## 2  2022-01-29 new_deaths     1394     1098      296 0.23756019
## 3  2022-01-23 new_deaths      868      709      159 0.20164870
## 4  2022-01-22 new_deaths     1176     1028      148 0.13430127
## 5  2022-01-16 new_deaths      807      747       60 0.07722008
## 6  2022-01-25 new_deaths     3445     3220      225 0.06751688
## 7  2022-01-24 new_deaths     2679     2505      174 0.06712963
## 8  2022-01-27 new_deaths     2757     2592      165 0.06169377
## 9  2022-01-17 new_deaths     1429     1350       79 0.05685498
## 10 2022-01-26 new_deaths     3023     2858      165 0.05611291
## 11 2022-01-29  new_cases   195076   173891    21185 0.11483412
## 12 2022-01-30  new_cases   138089   124992    13097 0.09956629
## 13 2022-01-31  new_cases   620416   661083    40667 0.06346786
## 14 2022-02-04  new_cases   272825   289747    16922 0.06015941

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name   newValue   refValue absDelta    pctDelta
## 1     KY tot_deaths    4003629    3992287    11342 0.002836948
## 2     AL tot_deaths    5972978    5963555     9423 0.001578850
## 3     NC tot_deaths    6808527    6798521    10006 0.001470708
## 4     FL  tot_cases 1290393798 1286243847  4149951 0.003221214
## 5     MD  tot_cases  232491171  231793719   697452 0.003004414
## 6     KY  tot_cases  252489934  252077588   412346 0.001634453
## 7     FL new_deaths      68042      66007     2035 0.030362032
## 8     KY new_deaths      13402      13063      339 0.025618742
## 9     AL new_deaths      17741      17371      370 0.021075416
## 10    NC new_deaths      21278      21097      181 0.008542773
## 11    RI new_deaths       3358       3354        4 0.001191895
## 12    MD  new_cases     984492     961805    22687 0.023312989
## 13    KY  new_cases    1208554    1193647    14907 0.012411118
## 14    TN  new_cases    1912511    1926401    13890 0.007236425
## 15    FL  new_cases    5648704    5629602    19102 0.003387388
## 16    NC  new_cases    2478266    2470242     8024 0.003242998
## 17    SC  new_cases    1408611    1405271     3340 0.002373945
## 18    RI  new_cases     348326     347901      425 0.001220866
## 19    PW  new_cases       2498       2495        3 0.001201682
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 45,540
## Columns: 15
## $ date           <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-0~
## $ state          <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "~
## $ tot_cases      <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 28~
## $ conf_cases     <dbl> 135705, NA, 3685032, NA, NA, 1130917, NA, 176228, NA, 7~
## $ prob_cases     <dbl> 27860, NA, 0, NA, NA, 0, NA, 103954, NA, 108997, 0, NA,~
## $ new_cases      <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 5~
## $ pnew_case      <dbl> 220, 0, 0, 0, 0, 0, 0, 559, 0, 443, 0, 0, 0, 0, NA, 0, ~
## $ tot_deaths     <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408~
## $ conf_death     <dbl> NA, 3616, 62011, 9604, NA, 19306, NA, 4739, NA, 10976, ~
## $ prob_death     <dbl> NA, 149, 0, 218, NA, 2030, NA, 1991, NA, 1432, NA, 416,~
## $ new_deaths     <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, ~
## $ pnew_death     <dbl> 0, 0, 0, 1, 0, 16, 0, 7, 0, 2, 0, 0, 0, 0, NA, 0, 0, 4,~
## $ created_at     <chr> "12/02/2021 02:35:20 PM", "08/19/2020 12:00:00 AM", "06~
## $ consent_cases  <chr> "Agree", "N/A", "Agree", "N/A", "Not agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Not agree", "A~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-02-05        inp   108309   114478     6169 0.05538025
## 2 2022-02-05   hosp_ped     3323     3585      262 0.07585408
## 3 2021-11-24   hosp_ped     1387     1306       81 0.06015596
## 4 2022-02-05 hosp_adult   104794   110893     6099 0.05655417

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state     name newValue refValue absDelta    pctDelta
## 1     NH hosp_ped      725      811       86 0.111979167
## 2     ME hosp_ped     1373     1431       58 0.041369472
## 3     WV hosp_ped     4435     4554      119 0.026476805
## 4     VT hosp_ped      348      357        9 0.025531915
## 5     AR hosp_ped    10602    10393      209 0.019909502
## 6     KS hosp_ped     3856     3929       73 0.018754014
## 7     SC hosp_ped     7275     7393      118 0.016089446
## 8     VI hosp_ped       81       80        1 0.012422360
## 9     MA hosp_ped     9296     9412      116 0.012401112
## 10    ID hosp_ped     3155     3120       35 0.011155378
## 11    KY hosp_ped    15228    15375      147 0.009606901
## 12    NJ hosp_ped    15981    15838      143 0.008988340
## 13    IN hosp_ped    14697    14787       90 0.006105006
## 14    UT hosp_ped     7026     6998       28 0.003993155
## 15    NV hosp_ped     3856     3871       15 0.003882490
## 16    ND hosp_ped     2898     2909       11 0.003788531
## 17    TN hosp_ped    17497    17563       66 0.003764974
## 18    AL hosp_ped    17263    17319       56 0.003238679
## 19    NC hosp_ped    23574    23649       75 0.003176418
## 20    OR hosp_ped     7333     7356       23 0.003131595
## 21    MO hosp_ped    31461    31363       98 0.003119827
## 22    MS hosp_ped     8953     8926       27 0.003020303
## 23    PA hosp_ped    43632    43509      123 0.002823011
## 24    GA hosp_ped    42185    42079      106 0.002515902
## 25    IA hosp_ped     6153     6168       15 0.002434867
## 26    HI hosp_ped     1909     1913        4 0.002093145
## 27    AZ hosp_ped    22800    22847       47 0.002059281
## 28    NE hosp_ped     6181     6170       11 0.001781232
## 29    WA hosp_ped    10469    10484       15 0.001431776
## 30    CO hosp_ped    17474    17499       25 0.001429674
## 31    WI hosp_ped     8578     8568       10 0.001166453
## 32    IL hosp_ped    35034    35073       39 0.001112585
## 33    OK hosp_ped    20546    20524       22 0.001071342
## 34    RI hosp_ped     2843     2846        3 0.001054667
## 35    PR hosp_ped    16962    16979       17 0.001001738
## 36    AK hosp_ped     1996     1998        2 0.001001502
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 38,675
## Columns: 117
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 27,992
## Columns: 82
## $ date                                   <date> 2022-02-19, 2022-02-19, 2022-0~
## $ MMWR_week                              <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7~
## $ state                                  <chr> "NC", "TN", "MN", "MI", "SD", "~
## $ Distributed                            <dbl> 20744900, 12186030, 11914970, 1~
## $ Distributed_Janssen                    <dbl> 916100, 503900, 500200, 926300,~
## $ Distributed_Moderna                    <dbl> 7813760, 4644240, 4216760, 7835~
## $ Distributed_Pfizer                     <dbl> 12015040, 7037890, 7198010, 111~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 197795, 178441, 211272, 199329,~
## $ Distributed_Per_100k_12Plus            <dbl> 230870, 208823, 249367, 231608,~
## $ Distributed_Per_100k_18Plus            <dbl> 253377, 229098, 274762, 253818,~
## $ Distributed_Per_100k_65Plus            <dbl> 1184680, 1065780, 1294570, 1127~
## $ vxa                                    <dbl> 16040239, 9551129, 9853584, 150~
## $ Administered_12Plus                    <dbl> 15576577, 9369683, 9460637, 146~
## $ Administered_18Plus                    <dbl> 14630091, 8914389, 8826086, 138~
## $ Administered_65Plus                    <dbl> 4239236, 2778123, 2487485, 4293~
## $ Administered_Janssen                   <dbl> 508845, 259901, 353693, 459665,~
## $ Administered_Moderna                   <dbl> 5969173, 3654211, 3581985, 5900~
## $ Administered_Pfizer                    <dbl> 9561290, 5583600, 5913885, 8724~
## $ Administered_Unk_Manuf                 <dbl> 931, 53417, 4021, 2106, 133, 32~
## $ Admin_Per_100k                         <dbl> 152938, 139858, 174720, 151062,~
## $ Admin_Per_100k_12Plus                  <dbl> 173352, 160562, 198000, 170666,~
## $ Admin_Per_100k_18Plus                  <dbl> 178691, 167591, 203531, 176626,~
## $ Admin_Per_100k_65Plus                  <dbl> 242091, 242972, 270267, 243193,~
## $ Recip_Administered                     <dbl> 15939232, 9383280, 9868373, 153~
## $ Administered_Dose1_Recip               <dbl> 8596653, 4180275, 4183752, 6576~
## $ Administered_Dose1_Pop_Pct             <dbl> 82.0, 61.2, 74.2, 65.9, 74.7, 0~
## $ Administered_Dose1_Recip_12Plus        <dbl> 8331132, 4079234, 3968078, 6351~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 92.7, 69.9, 83.0, 73.9, 86.3, 0~
## $ Administered_Dose1_Recip_18Plus        <dbl> 7823417, 3850407, 3680383, 5966~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 72.4, 84.9, 76.1, 88.9, 0~
## $ Administered_Dose1_Recip_65Plus        <dbl> 2154949, 1047531, 937204, 16884~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.6, 95.0, 95.0, 95.0, 0~
## $ vxc                                    <dbl> 6201249, 3646584, 3830382, 5889~
## $ vxcpoppct                              <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 0~
## $ Series_Complete_12Plus                 <dbl> 6011177, 3568185, 3651613, 5701~
## $ Series_Complete_12PlusPop_Pct          <dbl> 66.9, 61.1, 76.4, 66.3, 69.0, 0~
## $ vxcgte18                               <dbl> 5622542, 3375377, 3382210, 5355~
## $ vxcgte18pct                            <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 0~
## $ vxcgte65                               <dbl> 1498685, 956337, 876804, 153840~
## $ vxcgte65pct                            <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 0~
## $ Series_Complete_Janssen                <dbl> 477185, 232189, 326034, 416641,~
## $ Series_Complete_Moderna                <dbl> 2152155, 1299423, 1287594, 2139~
## $ Series_Complete_Pfizer                 <dbl> 3571765, 2103546, 2215307, 3333~
## $ Series_Complete_Unk_Manuf              <dbl> 144, 11426, 1447, 1082, 34, 0, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 477158, 232135, 326016, 416612,~
## $ Series_Complete_Moderna_12Plus         <dbl> 2152040, 1299371, 1287540, 2138~
## $ Series_Complete_Pfizer_12Plus          <dbl> 3381836, 2025317, 2036626, 3144~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 143, 11362, 1431, 1073, 34, 0, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 475728, 231891, 325496, 416312,~
## $ Series_Complete_Moderna_18Plus         <dbl> 2149019, 1298802, 1285260, 2138~
## $ Series_Complete_Pfizer_18Plus          <dbl> 2997656, 1833427, 1770066, 2799~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 139, 11257, 1388, 986, 34, 0, 5~
## $ Series_Complete_Janssen_65Plus         <dbl> 54321, 35691, 50477, 70861, 498~
## $ Series_Complete_Moderna_65Plus         <dbl> 720300, 474871, 369087, 768663,~
## $ Series_Complete_Pfizer_65Plus          <dbl> 723999, 439839, 456889, 698286,~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 65, 5936, 351, 592, 21, 0, 2511~
## $ Additional_Doses                       <dbl> 1544360, 1529958, 2125396, 2985~
## $ Additional_Doses_Vax_Pct               <dbl> 24.9, 42.0, 55.5, 50.7, 39.8, 2~
## $ Additional_Doses_12Plus                <dbl> 1544252, 1529687, 2125156, 2985~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 25.7, 42.9, 58.2, 52.4, 41.2, 2~
## $ Additional_Doses_18Plus                <dbl> 1500845, 1502838, 2049913, 2906~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 26.7, 44.5, 60.6, 54.3, 43.1, 2~
## $ Additional_Doses_50Plus                <dbl> 1017165, 1059552, 1281534, 1976~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 33.8, 56.3, 72.6, 64.9, 54.8, 4~
## $ Additional_Doses_65Plus                <dbl> 578981, 632802, 708477, 1135879~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 38.6, 66.2, 80.8, 73.8, 62.9, 5~
## $ Additional_Doses_Moderna               <dbl> 680325, 649042, 857058, 1316220~
## $ Additional_Doses_Pfizer                <dbl> 836934, 856740, 1239887, 162309~
## $ Additional_Doses_Janssen               <dbl> 27082, 20983, 28141, 46218, 254~
## $ Additional_Doses_Unk_Manuf             <dbl> 19, 3193, 310, 106, 9, 22, 648,~
## $ Administered_Dose1_Recip_5Plus         <dbl> 8594663, 4179589, 4181728, 6576~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 87.0, 65.1, 79.1, 69.8, 80.3, 0~
## $ Series_Complete_5Plus                  <dbl> 6200658, 3646444, 3829701, 5889~
## $ Series_Complete_5PlusPop_Pct           <dbl> 62.8, 56.8, 72.4, 62.5, 64.1, 0~
## $ Administered_5Plus                     <dbl> 16037693, 9550281, 9850893, 150~
## $ Admin_Per_100k_5Plus                   <dbl> 162353, 148745, 186287, 160139,~
## $ Distributed_Per_100k_5Plus             <dbl> 210004, 189797, 225320, 211315,~
## $ Series_Complete_Moderna_5Plus          <dbl> 2152112, 1299406, 1287586, 2138~
## $ Series_Complete_Pfizer_5Plus           <dbl> 3571235, 2103464, 2214653, 3333~
## $ Series_Complete_Janssen_5Plus          <dbl> 477168, 232150, 326019, 416627,~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 143, 11424, 1443, 1081, 34, 0, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  1.84e+10    3.17e+8   7.79e+7 910251       44781    
## 2 after   1.83e+10    3.15e+8   7.73e+7 905741       38709    
## 3 pctchg  4.83e- 3    4.32e-3   6.93e-3      0.00495     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 38,709
## Columns: 6
## $ date       <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-05-13~
## $ state      <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "NC",~
## $ tot_cases  <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 280182~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408, 55~
## $ new_cases  <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 537, ~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, 11, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult    hosp_ped          n
##   <chr>    <dbl>      <dbl>       <dbl>      <dbl>
## 1 before 4.51e+7    3.88e+7 945228      38675     
## 2 after  4.49e+7    3.86e+7 927959      37083     
## 3 pctchg 4.84e-3    4.63e-3      0.0183     0.0412
## 
## 
## Processed for cdcHosp:
## Rows: 37,083
## Columns: 5
## $ date       <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state      <chr> "HI", "NE", "IA", "NH", "HI", "DC", "KS", "NM", "ME", "NE",~
## $ inp        <dbl> 111, 376, 497, 45, 110, 166, 474, 189, 23, 316, 546, 3246, ~
## $ hosp_adult <dbl> 111, 367, 487, 44, 108, 149, 454, 186, 23, 315, 534, 3104, ~
## $ hosp_ped   <dbl> 0, 9, 10, 1, 2, 17, 5, 3, 0, 6, 12, 55, 8, 0, 1, 8, 2, 8, 6~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 2.66e+11 1.13e+11 1003870.    3.03e+10 1559557.    1.06e+11 1202494.   
## 2 after  1.28e+11 5.46e+10  843159.    1.46e+10 1396120.    5.14e+10 1020516.   
## 3 pctchg 5.20e- 1 5.16e- 1       0.160 5.16e- 1       0.105 5.17e- 1       0.151
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 22,083
## Columns: 9
## $ date        <date> 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-1~
## $ state       <chr> "NC", "TN", "MN", "MI", "SD", "OH", "MT", "WV", "VA", "IA"~
## $ vxa         <dbl> 16040239, 9551129, 9853584, 15086338, 1349798, 17152418, 1~
## $ vxc         <dbl> 6201249, 3646584, 3830382, 5889772, 527824, 6712161, 59625~
## $ vxcpoppct   <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 57.4, 55.8, 56.6, 71.7, 60.9~
## $ vxcgte65    <dbl> 1498685, 956337, 876804, 1538402, 140420, 1779459, 175563,~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 87.0, 85.0, 83.5, 91.2, 92.0~
## $ vxcgte18    <dbl> 5622542, 3375377, 3382210, 5355218, 476217, 6118597, 54652~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 67.2, 65.0, 65.8, 81.0, 71.7~
## 
## Integrated per capita data file:
## Rows: 38,973
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220220, ovrWriteError=FALSE)

The latest hospital data are downloaded:

# Run for latest data, save as RDS
indivHosp_20220221 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220221.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 399,863
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         7503
## 2 Critical Access Hospitals 106952
## 3 Long Term                  27474
## 4 Short Term                257934
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       25
## 2 GU      160
## 3 MP       80
## 4 PR     4400
## 5 VI      160
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        11667   387469             727 399863
## 2 all_adult_hospital_inpatient_bed_occupi~  3328   364400           32135 399863
## 3 icu_beds_used_7_day_avg                   1649   350757           47457 399863
## 4 inpatient_beds_7_day_avg                  1730   396567            1566 399863
## 5 staffed_icu_adult_patients_confirmed_an~  4251   279744          115868 399863
## 6 total_adult_patients_hospitalized_confi~  2372   278715          118776 399863
## 7 total_beds_7_day_avg                      6632   392858             373 399863
## 8 total_icu_beds_7_day_avg                  2064   377884           19915 399863
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220221, ovrWriteError=FALSE)

The post-processing capabilities are included:

# Create pivoted burden data
burdenPivotList_220220 <- postProcessCDCDaily(cdc_daily_220220, 
                                              dataThruLabel="Jan 2022", 
                                              keyDatesBurden=c("2022-01-31", "2021-07-31", 
                                                               "2021-01-31", "2020-07-31"
                                                               ),
                                              keyDatesVaccine=c("2021-12-31", "2021-09-30", 
                                                                "2021-06-30", "2021-03-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

The hospital summaries are also added:

# Can be run only as-needed
dfStateAgeBucket2019 <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
    filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
    bucketPopStateAge(popVar="pop2019")
## Rows: 13572 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): SUMLEV, NAME
## dbl (16): REGION, DIVISION, STATE, SEX, AGE, ESTBASE2010_CIV, POPEST2010_CIV...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## 
## *** File has been checked for uniqueness by: NAME SEX AGE 
## 
## [1] TRUE
## [1] TRUE
## [1] TRUE

## 
## PASSED CHECK: United States total is the sum of states and DC 
## 
## 
## PASSED CHECK: Age 999 total is the sum of the ages 
## 
## 
## PASSED CHECK: Sex 0 total is the sum of the sexes

# Create hospitalized per capita data
hospPerCap_220220 <- hospAgePerCapita(dfStateAgeBucket2019, 
                                      lst=burdenPivotList_220220, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

The one-page CFR plot capability is included:

# Create CFR plots for select states
cfrStates <- list("FL"=list(keyState="FL", minDate="2020-08-01", multDeath=70), 
                  "LA"=list(keyState="LA", minDate="2020-08-01", multDeath=80), 
                  "CA"=list(keyState="CA", minDate="2020-08-01", multDeath=100), 
                  "IL"=list(keyState="IL", minDate="2020-08-01", multDeath=100)
                  )
purrr::walk(cfrStates, .f=function(x) onePageCFRPlot(burdenPivotList_220220$dfPivot, 
                                                     keyState=x$keyState, 
                                                     minDate=x$minDate, 
                                                     multDeath=x$multDeath
                                                     )
            )

The peaks and valleys plots are included:

# Burden data
cdc_daily_220220$dfPerCapita %>%
    mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
    makePeakValley(numVar=c("new_deaths", "new_cases", "inp"), 
                   windowWidth = 71, 
                   rollMean=7, 
                   facetVar=c("regn"), 
                   fnNumVar=list("new_deaths"=function(x) x, 
                                 "new_cases"=function(x) x/1000,
                                 "inp"=function(x) x/1000
                                 ), 
                   fnPeak=list("new_deaths"=function(x) x+100, 
                               "new_cases"=function(x) x+10, 
                               "inp"=function(x) x+10
                               ),
                   fnValley=list("new_deaths"=function(x) x-100, 
                                 "new_cases"=function(x) x-5, 
                                 "inp"=function(x) x-5
                                 ),
                   useTitle=c("new_deaths"="US coronavirus deaths", 
                              "new_cases"="US coronavirus cases", 
                              "inp"="US coronavirus total hospitalized"
                              ), 
                   yLab=c("new_deaths"="Rolling 7-day mean deaths", 
                          "new_cases"="Rolling 7-day mean cases (000)", 
                          "inp"="Rolling 7-day mean in hospital (000)"
                          )
                   )
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## # A tibble: 3,107 × 11
##    date       regn         new_d…¹ new_c…²   inp new_d…³ new_c…⁴ inp_i…⁵ new_d…⁶
##    <date>     <chr>          <dbl>   <dbl> <dbl> <lgl>   <lgl>   <lgl>   <lgl>  
##  1 2020-01-01 North Centr…      NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  2 2020-01-01 South             NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  3 2020-01-01 West              NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  4 2020-01-02 North Centr…      NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  5 2020-01-02 South             NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  6 2020-01-02 West              NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  7 2020-01-03 North Centr…      NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  8 2020-01-03 South             NA      NA    NA FALSE   FALSE   FALSE   FALSE  
##  9 2020-01-03 West              NA      NA    NA FALSE   FALSE   FALSE   FALSE  
## 10 2020-01-04 North Centr…       0       0     0 FALSE   FALSE   FALSE   FALSE  
## # … with 3,097 more rows, 2 more variables: new_cases_isValley <lgl>,
## #   inp_isValley <lgl>, and abbreviated variable names ¹​new_deaths, ²​new_cases,
## #   ³​new_deaths_isPeak, ⁴​new_cases_isPeak, ⁵​inp_isPeak, ⁶​new_deaths_isValley
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
# Vaccinations data for states with 8+ million population
cdc_daily_220220$dfPerCapita %>%
    inner_join(getStateData(), by=c("state")) %>%
    filter(pop >= 8000000) %>%
    select(date, state, vxa, vxc) %>%
    arrange(date, state) %>%
    group_by(state) %>%
    mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
    ungroup() %>%
    mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
    filter(date >= "2020-12-01") %>%
    makePeakValley(numVar=c("vxc", "vxa"), 
                   windowWidth = 29, 
                   rollMean=21, 
                   facetVar=c("state"), 
                   fnNumVar=list("vxa"=function(x) x/1000, 
                                 "vxc"=function(x) x/1000
                                 ), 
                   fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400, 
                               "vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
                               ),
                   fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400, 
                                 "vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
                                 ),
                   fnGroupFacet=TRUE,
                   useTitle=c("vxa"="Vaccines adminsitered (US)", 
                              "vxc"="Became fully vaccinated (US)"
                              ), 
                   yLab=c("vxa"="Rolling 21-day mean administered (000)",
                          "vxc"="Rolling 21-day mean completed (000)"
                          )
                   )
## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 5,364 × 8
##    date       state   vxc   vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 5,354 more rows
## # ℹ Use `print(n = ...)` to see more rows

The hospital utlization plots are included:

indivHosp_20220221 %>% 
    filter(state %in% c(state.abb, "DC"), 
           collection_week==max(collection_week)
    ) %>% 
    pull(hospital_pk) %>%
    plotHospitalUtilization(df=indivHosp_20220221, keyHosp=., plotTitle="US Hospitals Summed")

Imputed hospital utilization data are also created, using functional form:

# Impute values for hospital capacity
imputeNACapacity <- function(df, 
                             keyStates=c(state.abb, "DC"), 
                             varMapper=hhsMapper, 
                             varsToImpute=c("total_beds", "adult_beds"), 
                             varUsedToImpute=c("inpatient_beds")
                             ) {
    
    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # keyState: states to include for filtering
    # varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
    # varsToImpute: variables to be imputed
    # varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
    
    df %>%
        filter(state %in% all_of(keyStates)) %>%
        colSelector(c("state", "collection_week", "hospital_pk", names(varMapper))) %>%
        colRenamer(varMapper) %>%
        mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), NA, ifelse(x==-999999, NA, x)))) %>%
        arrange(hospital_pk, collection_week) %>%
        group_by(hospital_pk) %>%
        mutate(across(all_of(varsToImpute), 
                      .fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=-999999)
                      )
               ) %>%
        group_by(state, collection_week) %>%
        summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
}

modStateHosp_20220221 <- imputeNACapacity(indivHosp_20220221)

The function is split so that it is more generic:

# Select and filter as needed
skinnyHHS <- function(df, 
                      keyStates=c(state.abb, "DC"), 
                      idCols=c("state", "collection_week", "hospital_pk"),
                      varMapper=hhsMapper
                      ) {

    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # keyState: states to include for filtering
    # varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
    
    df %>%
        filter(state %in% all_of(keyStates)) %>%
        colSelector(c(all_of(idCols), names(varMapper))) %>%
        colRenamer(varMapper)
        
}

# Impute values for hospital capacity
imputeNACapacity <- function(df, 
                             extraNA=c(-999999),
                             convertAllNA=TRUE,
                             idVars=c("hospital_pk"), 
                             sortVars=c("collection_week"),
                             varsToImpute=c("total_beds", "adult_beds"), 
                             varUsedToImpute=c("inpatient_beds")
                             ) {
    
    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # extraNA: values that should be treated as NA
    # convertAllNA: boolean, should all extraNA values be converted in all numeric columns?
    #               if FALSE, extraNA values will not be converted, though imputing will treat as NA
    # varsToImpute: variables to be imputed
    # varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
    
    # Convert NA if requested
    if(isTRUE(convertAllNA)) {
        df <- df %>%
            mutate(across(where(is.numeric), 
                          .fns=function(x) ifelse(is.na(x), NA, ifelse(x %in% all_of(extraNA), NA, x))
                          )
                   )
    }
    
    # Impute values and return data
    df %>%
        arrange(across(all_of(c(idVars, sortVars)))) %>%
        group_by(across(all_of(idVars))) %>%
        mutate(across(all_of(varsToImpute), 
                      .fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=extraNA)
                      )
               ) %>%
        ungroup()
    
}

sumImputedHHS <- function(df, 
                          groupVars=c("state", "collection_week")) {
    
    # FUNCTION ARGUMENTS:
    # df: the initial data frame
    # groupVars: variables for summing the data to

    df %>%
        group_by(across(all_of(groupVars))) %>%
        summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
    
}

identical(skinnyHHS(indivHosp_20220221) %>%
              imputeNACapacity() %>%
              sumImputedHHS(), 
          modStateHosp_20220221
          )
## [1] TRUE

Updated maps with imputed capacity are created:

modStateHosp_20220221 <- skinnyHHS(indivHosp_20220221) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220221, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to January 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
createGeoMap(modStateHosp_20220221 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to January 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

Example Data Update

The function is run to download and process the latest data:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220304.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220304.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220304.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220220")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220220")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220220")$dfRaw$vax
                    )

cdc_daily_220304 <- readRunCDCDaily(thruLabel="Mar 2, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2020-03-03  tot_cases      175      188       13 0.07162534
## 2  2022-02-13 new_deaths      615      446      169 0.31856739
## 3  2022-02-06 new_deaths      609      472      137 0.25346901
## 4  2022-02-12 new_deaths      891      695      196 0.24716267
## 5  2022-02-05 new_deaths     1158      989      169 0.15742897
## 6  2022-01-30 new_deaths      869      796       73 0.08768769
## 7  2022-02-07 new_deaths     3177     3000      177 0.05730937
## 8  2022-02-08 new_deaths     3704     3504      200 0.05549390
## 9  2022-02-03 new_deaths     2653     2515      138 0.05340557
## 10 2022-01-29 new_deaths     1469     1394       75 0.05239260
## 11 2022-02-11 new_deaths     2775     2638      137 0.05061888
## 12 2020-03-03  new_cases       51       64       13 0.22608696
## 13 2022-02-12  new_cases    66377    55089    11288 0.18586271
## 14 2022-02-13  new_cases    47803    40950     6853 0.15442858
## 15 2022-01-30  new_cases   155259   138089    17170 0.11706233
## 16 2022-02-05  new_cases   102256    91295    10961 0.11326214
## 17 2022-02-14  new_cases   178028   199342    21314 0.11296075
## 18 2022-02-11  new_cases   155537   172496    16959 0.10339813
## 19 2022-01-29  new_cases   215839   195076    20763 0.10105740
## 20 2020-03-07  new_cases      146      160       14 0.09150327
## 21 2021-10-31  new_cases    22766    20850     1916 0.08785767
## 22 2021-11-06  new_cases    32140    29452     2688 0.08728406
## 23 2021-10-24  new_cases    25952    23899     2053 0.08236545
## 24 2021-11-07  new_cases    28368    26379     1989 0.07266152
## 25 2020-03-06  new_cases      130      121        9 0.07171315
## 26 2021-10-23  new_cases    33628    31349     2279 0.07014790
## 27 2022-01-22  new_cases   320403   299989    20414 0.06581000
## 28 2022-02-06  new_cases    96184    90271     5913 0.06342549
## 29 2022-01-18  new_cases   861976   917498    55522 0.06240271
## 30 2020-03-09  new_cases      390      415       25 0.06211180
## 31 2022-01-31  new_cases   583405   620416    37011 0.06148921
## 32 2022-01-23  new_cases   310096   291779    18317 0.06086646
## 33 2021-11-14  new_cases    30649    28992     1657 0.05556580
## 34 2021-11-20  new_cases    42759    40531     2228 0.05349982
## 35 2021-12-25  new_cases   126095   119545     6550 0.05333008
## 36 2021-10-30  new_cases    31410    29822     1588 0.05186830
## 37 2021-05-24  new_cases    15400    16206      806 0.05100297

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     RI tot_deaths   1395734   1419362    23628 0.016786639
## 2     FL tot_deaths  22083833  22194156   110323 0.004983198
## 3     KY tot_deaths   4208985   4192009    16976 0.004041427
## 4     NC tot_deaths   7123584   7111646    11938 0.001677247
## 5     AL tot_deaths   6231481   6222060     9421 0.001512983
## 6     RI  tot_cases  75530417  79533518  4003101 0.051631619
## 7     ME  tot_cases  38751314  36951188  1800126 0.047557900
## 8     WA  tot_cases 263650336 264272454   622118 0.002356852
## 9     KY  tot_cases 270113183 269767213   345970 0.001281654
## 10    AL new_deaths     18381     17877      504 0.027800761
## 11    FL new_deaths     70406     68581     1825 0.026261449
## 12    WA new_deaths     11615     11316      299 0.026078235
## 13    KY new_deaths     13885     13565      320 0.023315118
## 14    NC new_deaths     22277     22148      129 0.005807541
## 15    ME  new_cases    225203    212435    12768 0.058349595
## 16    RI  new_cases    336543    354045    17502 0.050687240
## 17    WA  new_cases   1396813   1410596    13783 0.009819018
## 18    KY  new_cases   1265367   1258310     7057 0.005592633
## 19    SD  new_cases    234285    234961      676 0.002881218
## 20    NC  new_cases   2563976   2559793     4183 0.001632782
## 21    SC  new_cases   1451483   1449247     2236 0.001541681
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 46,260
## Columns: 15
## $ date           <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-0~
## $ state          <chr> "KS", "UT", "CO", "AR", "AR", "CO", "PW", "UT", "MA", "~
## $ tot_cases      <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 0, 636992, 7~
## $ conf_cases     <dbl> 241035, 359641, 411869, NA, NA, 1117524, NA, 636992, 65~
## $ prob_cases     <dbl> 56194, 0, 26876, NA, NA, 105369, NA, 0, 45550, 321, NA,~
## $ new_cases      <dbl> 0, 1060, 677, 0, 547, 6962, 0, 0, 451, 619, 69, 24010, ~
## $ pnew_case      <dbl> 0, 0, 60, NA, 0, 1247, 0, 0, 46, 1, 10, 4196, 264, 3202~
## $ tot_deaths     <dbl> 4851, 1785, 5952, 0, 674, 10953, 0, 3787, 17818, 805, 8~
## $ conf_death     <dbl> NA, 1729, 5218, NA, NA, 9666, NA, 3635, 17458, 624, NA,~
## $ prob_death     <dbl> NA, 56, 734, NA, NA, 1287, NA, 152, 360, 181, NA, NA, 1~
## $ new_deaths     <dbl> 0, 11, 1, 0, 11, 20, 0, 0, 5, 3, 0, 345, 8, 190, 0, 3, ~
## $ pnew_death     <dbl> 0, 2, 0, NA, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, NA,~
## $ created_at     <chr> "03/12/2021 03:20:13 PM", "02/13/2021 02:50:08 PM", "03~
## $ consent_cases  <chr> "Agree", "Agree", "Agree", "Not agree", "Not agree", "A~
## $ consent_deaths <chr> "N/A", "Agree", "Agree", "Not agree", "Not agree", "Agr~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 11
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-02-20        inp    58908    62620     3712 0.06108880
## 2 2022-02-20 hosp_adult    56750    60478     3728 0.06360255

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     ND        inp   110569   109813      756 0.006860814
## 2     WV   hosp_ped     4497     4703      206 0.044782609
## 3     ME   hosp_ped     1594     1538       56 0.035759898
## 4     MA   hosp_ped    10034     9724      310 0.031379694
## 5     IN   hosp_ped    15429    15261      168 0.010948192
## 6     KY   hosp_ped    15750    15913      163 0.010295929
## 7     VA   hosp_ped    14705    14555      150 0.010252905
## 8     NJ   hosp_ped    16251    16415      164 0.010041021
## 9     NV   hosp_ped     4105     4067       38 0.009300049
## 10    SC   hosp_ped     7732     7661       71 0.009224972
## 11    AL   hosp_ped    17872    17976      104 0.005802276
## 12    VT   hosp_ped      360      362        2 0.005540166
## 13    KS   hosp_ped     4025     4005       20 0.004981320
## 14    NM   hosp_ped     6428     6457       29 0.004501358
## 15    IA   hosp_ped     6509     6481       28 0.004311008
## 16    NH   hosp_ped      761      758        3 0.003949967
## 17    FL   hosp_ped    82260    82509      249 0.003022413
## 18    TN   hosp_ped    18633    18581       52 0.002794647
## 19    WY   hosp_ped      784      786        2 0.002547771
## 20    CO   hosp_ped    18126    18084       42 0.002319801
## 21    SD   hosp_ped     3899     3891        8 0.002053915
## 22    GA   hosp_ped    43658    43742       84 0.001922197
## 23    AR   hosp_ped    10931    10911       20 0.001831334
## 24    UT   hosp_ped     7634     7621       13 0.001704359
## 25    CT   hosp_ped     5640     5649        9 0.001594472
## 26    HI   hosp_ped     2016     2019        3 0.001486989
## 27    MS   hosp_ped     9380     9368       12 0.001280137
## 28    AZ   hosp_ped    23979    23949       30 0.001251878
## 29    IL   hosp_ped    36164    36121       43 0.001189735
## 30    MN   hosp_ped    13210    13224       14 0.001059242
## 31    ND hosp_adult   104829   102042     2787 0.026944328
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 39,269
## Columns: 117
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 28,760
## Columns: 82
## $ date                                   <date> 2022-03-03, 2022-03-03, 2022-0~
## $ MMWR_week                              <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9~
## $ state                                  <chr> "NE", "NC", "TX", "CA", "AL", "~
## $ Distributed                            <dbl> 3775510, 20928600, 58996495, 86~
## $ Distributed_Janssen                    <dbl> 149600, 917900, 2609300, 368570~
## $ Distributed_Moderna                    <dbl> 1331380, 7886660, 21192040, 307~
## $ Distributed_Pfizer                     <dbl> 2294530, 12124040, 35195155, 51~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 195177, 199546, 203465, 217827,~
## $ Distributed_Per_100k_12Plus            <dbl> 233430, 232915, 244787, 255803,~
## $ Distributed_Per_100k_18Plus            <dbl> 258892, 255621, 273182, 281107,~
## $ Distributed_Per_100k_65Plus            <dbl> 1208330, 1195170, 1579880, 1474~
## $ vxa                                    <dbl> 3086667, 16146189, 44500682, 71~
## $ Administered_12Plus                    <dbl> 2984836, 15667506, 42929166, 69~
## $ Administered_18Plus                    <dbl> 2784330, 14709026, 39448235, 63~
## $ Administered_65Plus                    <dbl> 818837, 4254447, 8966187, 14693~
## $ Administered_Janssen                   <dbl> 93421, 510563, 1535569, 2278802~
## $ Administered_Moderna                   <dbl> 1110596, 6003546, 16331212, 267~
## $ Administered_Pfizer                    <dbl> 1876552, 9631145, 26629492, 426~
## $ Administered_Unk_Manuf                 <dbl> 6098, 935, 4409, 15243, 477, 21~
## $ Admin_Per_100k                         <dbl> 159566, 153948, 153472, 181390,~
## $ Admin_Per_100k_12Plus                  <dbl> 184544, 174364, 178121, 205442,~
## $ Admin_Per_100k_18Plus                  <dbl> 190925, 179655, 182664, 208987,~
## $ Admin_Per_100k_65Plus                  <dbl> 262063, 242959, 240108, 251683,~
## $ Recip_Administered                     <dbl> 3099534, 16045766, 43251399, 71~
## $ Administered_Dose1_Recip               <dbl> 1343086, 8641769, 20646737, 323~
## $ Administered_Dose1_Pop_Pct             <dbl> 69.4, 82.4, 71.2, 81.8, 61.9, 6~
## $ Administered_Dose1_Recip_12Plus        <dbl> 1287672, 8369683, 19753177, 309~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 79.6, 93.1, 82.0, 91.9, 71.1, 7~
## $ Administered_Dose1_Recip_18Plus        <dbl> 1192672, 7856851, 17992902, 284~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 81.8, 95.0, 83.3, 92.9, 73.8, 7~
## $ Administered_Dose1_Recip_65Plus        <dbl> 306831, 2160730, 3621726, 59741~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc                                    <dbl> 1210303, 6231369, 17444705, 278~
## $ vxcpoppct                              <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 5~
## $ Series_Complete_12Plus                 <dbl> 1164264, 6032933, 16838920, 267~
## $ Series_Complete_12PlusPop_Pct          <dbl> 72.0, 67.1, 69.9, 79.5, 58.0, 6~
## $ vxcgte18                               <dbl> 1078877, 5641201, 15431387, 245~
## $ vxcgte18pct                            <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 6~
## $ vxcgte65                               <dbl> 284820, 1501131, 3209108, 51872~
## $ vxcgte65pct                            <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 8~
## $ Series_Complete_Janssen                <dbl> 87478, 478497, 1339798, 2070288~
## $ Series_Complete_Moderna                <dbl> 412033, 2159277, 6030460, 95834~
## $ Series_Complete_Pfizer                 <dbl> 709218, 3593448, 10073548, 1620~
## $ Series_Complete_Unk_Manuf              <dbl> 1574, 147, 899, 4865, 654, 375,~
## $ Series_Complete_Janssen_12Plus         <dbl> 87453, 478469, 1339351, 2069676~
## $ Series_Complete_Moderna_12Plus         <dbl> 411994, 2159160, 6029642, 95826~
## $ Series_Complete_Pfizer_12Plus          <dbl> 663259, 3395158, 9469060, 15076~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 1558, 146, 867, 4805, 654, 373,~
## $ Series_Complete_Janssen_18Plus         <dbl> 87386, 477036, 1337802, 2062395~
## $ Series_Complete_Moderna_18Plus         <dbl> 411823, 2156126, 6025533, 95567~
## $ Series_Complete_Pfizer_18Plus          <dbl> 578185, 3007897, 8067213, 12955~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 1483, 142, 839, 4500, 650, 366,~
## $ Series_Complete_Janssen_65Plus         <dbl> 6942, 54470, 177453, 201180, 36~
## $ Series_Complete_Moderna_65Plus         <dbl> 138470, 721440, 1524709, 262127~
## $ Series_Complete_Pfizer_65Plus          <dbl> 138496, 725155, 1506622, 236331~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 912, 66, 324, 1458, 415, 213, 1~
## $ Additional_Doses                       <dbl> 585237, 1574890, 6257276, 13507~
## $ Additional_Doses_Vax_Pct               <dbl> 48.4, 25.3, 35.9, 48.5, 34.4, 3~
## $ Additional_Doses_12Plus                <dbl> 585134, 1574750, 6256853, 13506~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 50.3, 26.1, 37.2, 50.5, 34.9, 3~
## $ Additional_Doses_18Plus                <dbl> 565479, 1527656, 6053696, 12967~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 52.4, 27.1, 39.2, 52.8, 36.3, 4~
## $ Additional_Doses_50Plus                <dbl> 364760, 1031758, 3720211, 72429~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 65.5, 34.3, 52.0, 63.8, 47.2, 5~
## $ Additional_Doses_65Plus                <dbl> 211563, 585640, 1945440, 368152~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 74.3, 39.0, 60.6, 71.0, 56.9, 6~
## $ Additional_Doses_Moderna               <dbl> 229241, 693169, 2749043, 586417~
## $ Additional_Doses_Pfizer                <dbl> 349233, 854191, 3412442, 742991~
## $ Additional_Doses_Janssen               <dbl> 6431, 27508, 95589, 213372, 152~
## $ Additional_Doses_Unk_Manuf             <dbl> 332, 22, 202, 522, 81, 490, 73,~
## $ Administered_Dose1_Recip_5Plus         <dbl> 1342804, 8639422, 20641353, 323~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 74.5, 87.5, 76.4, 87.0, 65.9, 7~
## $ Series_Complete_5Plus                  <dbl> 1210243, 6230533, 17443348, 278~
## $ Series_Complete_5PlusPop_Pct           <dbl> 67.1, 63.1, 64.6, 75.0, 53.5, 5~
## $ Administered_5Plus                     <dbl> 3086314, 16143045, 44494008, 71~
## $ Admin_Per_100k_5Plus                   <dbl> 171126, 163419, 164762, 192973,~
## $ Distributed_Per_100k_5Plus             <dbl> 209340, 211864, 218465, 231812,~
## $ Series_Complete_Moderna_5Plus          <dbl> 412011, 2159234, 6029941, 95831~
## $ Series_Complete_Pfizer_5Plus           <dbl> 709196, 3592673, 10072988, 1620~
## $ Series_Complete_Janssen_5Plus          <dbl> 87463, 478480, 1339521, 2069915~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 1573, 146, 898, 4864, 654, 373,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  1.93e+10    3.28e+8   7.85e+7 931901       45489    
## 2 after   1.92e+10    3.27e+8   7.80e+7 927317       39321    
## 3 pctchg  4.93e- 3    4.33e-3   6.98e-3      0.00492     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 39,321
## Columns: 6
## $ date       <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-08-22~
## $ state      <chr> "KS", "UT", "CO", "AR", "AR", "CO", "UT", "MA", "HI", "TX",~
## $ tot_cases  <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 636992, 704796, ~
## $ tot_deaths <dbl> 4851, 1785, 5952, 0, 674, 10953, 3787, 17818, 883, 33124, 7~
## $ new_cases  <dbl> 0, 1060, 677, 0, 547, 6962, 0, 451, 69, 24010, 1028, 18811,~
## $ new_deaths <dbl> 0, 11, 1, 0, 11, 20, 0, 5, 0, 345, 8, 190, 3, 15, 7, 8, 0, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult    hosp_ped          n
##   <chr>    <dbl>      <dbl>       <dbl>      <dbl>
## 1 before 4.57e+7    3.93e+7 965351      39269     
## 2 after  4.54e+7    3.91e+7 947960      37644     
## 3 pctchg 4.81e-3    4.60e-3      0.0180     0.0414
## 
## 
## Processed for cdcHosp:
## Rows: 37,644
## Columns: 5
## $ date       <date> 2020-10-18, 2020-10-13, 2020-10-12, 2020-10-08, 2020-10-06~
## $ state      <chr> "VT", "NH", "ID", "MT", "HI", "NH", "NC", "DC", "MA", "MT",~
## $ inp        <dbl> 2, 34, 221, 262, 124, 48, 1283, 156, 354, 207, 116, 102, 39~
## $ hosp_adult <dbl> 2, 34, 219, 259, 124, 48, 1246, 141, 347, 206, 109, 101, 38~
## $ hosp_ped   <dbl> 0, 0, 2, 3, 0, 0, 34, 15, 7, 1, 3, 1, 10, 0, 0, 1, 6, 6, 7,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 2.79e+11 1.18e+11 1050491.    3.15e+10 1622354.    1.11e+11 1255957.   
## 2 after  1.34e+11 5.72e+10  882060.    1.52e+10 1450421     5.37e+10 1065505.   
## 3 pctchg 5.20e- 1 5.16e- 1       0.160 5.16e- 1       0.106 5.17e- 1       0.152
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 22,695
## Columns: 9
## $ date        <date> 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-0~
## $ state       <chr> "NE", "NC", "TX", "CA", "AL", "SC", "WV", "MN", "CO", "KS"~
## $ vxa         <dbl> 3086667, 16146189, 44500682, 71671126, 6108052, 7287794, 2~
## $ vxc         <dbl> 1210303, 6231369, 17444705, 27867605, 2466221, 2880832, 10~
## $ vxcpoppct   <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 56.0, 56.8, 68.3, 69.3, 60.3~
## $ vxcgte65    <dbl> 284820, 1501131, 3209108, 5187220, 689667, 807207, 306687,~
## $ vxcgte65pct <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 86.1, 83.6, 95.0, 92.0, 89.5~
## $ vxcgte18    <dbl> 1078877, 5641201, 15431387, 24579521, 2300814, 2646739, 94~
## $ vxcgte18pct <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 65.6, 66.0, 78.2, 79.1, 71.1~
## 
## Integrated per capita data file:
## Rows: 39,534
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220304, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220304 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220304.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 409,797
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         7690
## 2 Critical Access Hospitals 109641
## 3 Long Term                  28161
## 4 Short Term                264305
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       27
## 2 GU      164
## 3 MP       82
## 4 PR     4506
## 5 VI      164
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        15604   393445             748 409797
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   373556           32923 409797
## 3 icu_beds_used_7_day_avg                   1649   359635           48513 409797
## 4 inpatient_beds_7_day_avg                  1730   406462            1605 409797
## 5 staffed_icu_adult_patients_confirmed_an~  4241   286438          119118 409797
## 6 total_adult_patients_hospitalized_confi~  2362   285557          121878 409797
## 7 total_beds_7_day_avg                     10392   399022             383 409797
## 8 total_icu_beds_7_day_avg                  2064   387368           20365 409797
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220304, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220304 <- postProcessCDCDaily(cdc_daily_220304, 
                                              dataThruLabel="Feb 2022", 
                                              keyDatesBurden=c("2022-02-28", "2021-08-31", 
                                                               "2021-02-28", "2020-08-31"
                                                               ),
                                              keyDatesVaccine=c("2022-02-28", "2021-10-31", 
                                                                "2021-06-30", "2021-02-28"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220304 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220304, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

Peaks and valleys are converted to functional form:

peakValleyCDCDaily <- function(df, 
                               burdenVars=c("new_deaths", "new_cases", "inp"), 
                               burdenWidth=71, 
                               burdenRollMean=7,
                               minPopVax=8000000, 
                               vaxVars=c("vxa", "vxc"), 
                               minDateVax="2020-12-01", 
                               vaxWidth=71, 
                               vaxRollMean=21
                               ) {
    
    # FUNCTION ARGUMENTS
    # df: data frame (can also pass a list that contains data frame "dfPerCapita")
    # burdenVars: variables to be used for burden peaks and valleys
    # burdenWidth: window size to be used for burden data
    # burdenRollMean: rolling mean to use for smoothing burden data
    # minPopVax: minimum population for state vaccines to be plotted
    # vaxVars: variables to be used for vaccines peaks and valleys
    # minDateVax: earliest day to use for vaccines plotting
    # vaxWidth: window size to be used for vaccines data
    # vaxRollMean: rolling mean to use for smoothing vaccines data

    # Only works for specified burdenVars and vaxVars (fix)
    if(!all.equal(sort(burdenVars), sort(c("new_deaths", "new_cases", "inp")))) stop("\nNot yet enabled - burden\n")
    if(!all.equal(sort(vaxVars), sort(c("vxa", "vxc")))) stop("\nNot yet enabled - vaccines\n")
    
    # Extract data frame from df if needed
    if("list" %in% class(df)) df <- df[["dfPerCapita"]]

    # Burden data
    df %>%
        mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
        makePeakValley(numVar=burdenVars, 
                       windowWidth = burdenWidth, 
                       rollMean=burdenRollMean, 
                       facetVar=c("regn"), 
                       fnNumVar=list("new_deaths"=function(x) x, 
                                     "new_cases"=function(x) x/1000,
                                     "inp"=function(x) x/1000
                                     ), 
                       fnPeak=list("new_deaths"=function(x) x+100, 
                                   "new_cases"=function(x) x+10, 
                                   "inp"=function(x) x+10
                                   ),
                       fnValley=list("new_deaths"=function(x) x-100, 
                                     "new_cases"=function(x) x-5, 
                                     "inp"=function(x) x-5
                                     ),
                       useTitle=c("new_deaths"="US coronavirus deaths", 
                                  "new_cases"="US coronavirus cases", 
                                  "inp"="US coronavirus total hospitalized"
                                  ), 
                       yLab=c("new_deaths"=paste0("Rolling ", burdenRollMean, "-day mean deaths"), 
                              "new_cases"=paste0("Rolling ", burdenRollMean, "-day mean cases (000)"), 
                              "inp"=paste0("Rolling ", burdenRollMean, "-day mean in hospital (000)")
                              )
                       )

    # Vaccinations data for states with at least threshold population
    df %>%
        inner_join(getStateData(), by=c("state")) %>%
        filter(pop >= minPopVax) %>%
        select(c("state", "date", all_of(vaxVars))) %>%
        arrange(date, state) %>%
        group_by(state) %>%
        mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
        ungroup() %>%
        mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
        filter(date >= minDateVax) %>%
        makePeakValley(numVar=vaxVars, 
                       windowWidth = vaxWidth, 
                       rollMean=vaxRollMean, 
                       facetVar=c("state"), 
                       fnNumVar=list("vxa"=function(x) x/1000, 
                                     "vxc"=function(x) x/1000
                                     ), 
                       fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400, 
                                   "vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
                                   ),
                       fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400, 
                                     "vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
                                     ),
                       fnGroupFacet=TRUE,
                       useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", minPopVax, ")"), 
                                  "vxc"=paste0("Became fully vaccinated (states with population >= ", minPopVax, ")")
                                  ), 
                       yLab=c("vxa"=paste0("Rolling ", vaxRollMean, "-day mean administered (000)"),
                              "vxc"=paste0("Rolling ", vaxRollMean,"-day mean completed (000)")
                              )
                       )
    
}

peakValleyCDCDaily(cdc_daily_220304)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 5,496 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 5,486 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity maps with imputed capacity are created:

modStateHosp_20220304 <- skinnyHHS(indivHosp_20220304) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220304, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to February 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
createGeoMap(modStateHosp_20220304 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to February 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

The latest data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220416.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220416.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220416.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220304")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220304")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220304")$dfRaw$vax
                    )

cdc_daily_220416 <- readRunCDCDaily(thruLabel="Apr 14, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-02-27 new_deaths      336      207      129 0.47513812
## 2  2022-02-26 new_deaths      553      416      137 0.28276574
## 3  2022-02-20 new_deaths      563      441      122 0.24302789
## 4  2021-07-30 new_deaths      651      521      130 0.22184300
## 5  2022-02-19 new_deaths      732      612      120 0.17857143
## 6  2022-02-13 new_deaths      693      615       78 0.11926606
## 7  2022-02-21 new_deaths     1089      967      122 0.11867704
## 8  2022-02-12 new_deaths      988      891       97 0.10324641
## 9  2022-02-06 new_deaths      674      609       65 0.10132502
## 10 2022-02-18 new_deaths     2283     2149      134 0.06046931
## 11 2022-02-05 new_deaths     1221     1158       63 0.05296343
## 12 2022-02-26  new_cases    26248    23158     3090 0.12508602
## 13 2021-10-31  new_cases    25456    22766     2690 0.11156733
## 14 2022-02-27  new_cases    18268    16411     1857 0.10709651
## 15 2022-02-28  new_cases    72092    80046     7954 0.10456296
## 16 2021-11-07  new_cases    31372    28368     3004 0.10056913
## 17 2021-11-06  new_cases    35485    32140     3345 0.09892791
## 18 2021-10-30  new_cases    34475    31410     3065 0.09304090
## 19 2021-11-14  new_cases    33631    30649     2982 0.09278158
## 20 2021-10-23  new_cases    36520    33628     2892 0.08245424
## 21 2021-10-24  new_cases    28146    25952     2194 0.08111206
## 22 2021-11-20  new_cases    45749    42759     2990 0.06756451
## 23 2021-11-21  new_cases    38274    35892     2382 0.06423429
## 24 2021-11-13  new_cases    53584    50305     3279 0.06312507
## 25 2021-10-25  new_cases    84093    88971     4878 0.05637221
## 26 2021-11-08  new_cases   116560   122589     6029 0.05042045

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     KY tot_deaths   4418134   4375794    42340 0.009629372
## 2     FL tot_deaths  23037457  22932268   105189 0.004576447
## 3     AL tot_deaths   6469661   6452309    17352 0.002685659
## 4     NC tot_deaths   7407674   7392943    14731 0.001990593
## 5     SC tot_deaths   5380045   5370547     9498 0.001766972
## 6     CO  tot_cases 314009823 311159444  2850379 0.009118743
## 7     DE  tot_cases  61116141  61473234   357093 0.005825839
## 8     KY  tot_cases 286467313 285415770  1051543 0.003677475
## 9     NC  tot_cases 592997222 592074229   922993 0.001557700
## 10    KY new_deaths     14951     13935     1016 0.070345496
## 11    DE new_deaths      2711      2573      138 0.052233157
## 12    AL new_deaths     19167     18407      760 0.040453505
## 13    FL new_deaths     72517     70789     1728 0.024116227
## 14    NC new_deaths     22958     22671      287 0.012579719
## 15    RI new_deaths      3441      3413       28 0.008170411
## 16    CO  new_cases   1345585   1312298    33287 0.025047754
## 17    KY  new_cases   1305049   1282281    22768 0.017599610
## 18    NC  new_cases   2612332   2592991    19341 0.007431239
## 19    DE  new_cases    257219    256051     1168 0.004551211
## 20    SC  new_cases   1463332   1461843     1489 0.001018059
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 48,840
## Columns: 15
## $ date           <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state          <chr> "KS", "AS", "AR", "MP", "AS", "HI", "MA", "PR", "GA", "~
## $ tot_cases      <dbl> 621273, 11, 56199, 37, 0, 661, 704796, 35112, 1187107, ~
## $ conf_cases     <dbl> 470516, NA, NA, 37, NA, NA, 659246, 34791, 937515, 3739~
## $ prob_cases     <dbl> 150757, NA, NA, 0, NA, NA, 45550, 321, 249592, 101649, ~
## $ new_cases      <dbl> 19414, 0, 547, 1, 0, 8, 451, 619, 3829, 1028, 0, 0, 276~
## $ pnew_case      <dbl> 6964, 0, 0, 0, 0, 0, 46, 1, 1144, 264, 0, 0, 317, 0, 0,~
## $ tot_deaths     <dbl> 7162, 0, 674, 2, 0, 17, 17818, 805, 21690, 7488, 0, 140~
## $ conf_death     <dbl> NA, NA, NA, 2, NA, NA, 17458, 624, 18725, 6379, NA, 980~
## $ prob_death     <dbl> NA, NA, NA, 0, NA, NA, 360, 181, 2965, 1109, NA, 4202, ~
## $ new_deaths     <dbl> 21, 0, 11, 0, 0, 0, 5, 3, 7, 8, 0, 0, 3, 0, 69, 34, 0, ~
## $ pnew_death     <dbl> 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ created_at     <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases  <chr> "Agree", NA, "Not agree", "Agree", NA, "Not agree", "Ag~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Not agree", "Agre~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-03-03        inp    39007    41066     2059 0.05142807
## 2 2022-03-03 hosp_adult    37433    39443     2010 0.05229200

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     NH   hosp_ped      876      785       91 0.109572547
## 2     WV   hosp_ped     4828     4653      175 0.036915937
## 3     MA   hosp_ped     9943    10225      282 0.027965093
## 4     AR   hosp_ped    10871    11128      257 0.023364698
## 5     NV   hosp_ped     4183     4271       88 0.020818547
## 6     SC   hosp_ped     7993     7878      115 0.014491840
## 7     KY   hosp_ped    16368    16155      213 0.013098423
## 8     AL   hosp_ped    18331    18119      212 0.011632373
## 9     VA   hosp_ped    14832    14994      162 0.010863005
## 10    DE   hosp_ped     4271     4236       35 0.008228518
## 11    ID   hosp_ped     3406     3433       27 0.007895891
## 12    NJ   hosp_ped    16512    16406      106 0.006440245
## 13    IN   hosp_ped    15652    15740       88 0.005606524
## 14    UT   hosp_ped     7997     8031       34 0.004242575
## 15    MD   hosp_ped    12691    12739       48 0.003775069
## 16    PR   hosp_ped    17374    17309       65 0.003748234
## 17    OK   hosp_ped    21962    22043       81 0.003681400
## 18    TN   hosp_ped    19088    19153       65 0.003399493
## 19    VT   hosp_ped      382      383        1 0.002614379
## 20    FL   hosp_ped    83322    83126      196 0.002355090
## 21    CO   hosp_ped    18479    18439       40 0.002166965
## 22    PA   hosp_ped    45833    45907       74 0.001613255
## 23    HI   hosp_ped     2117     2120        3 0.001416096
## 24    CA   hosp_ped    67230    67137       93 0.001384268
## 25    MO   hosp_ped    33571    33616       45 0.001339545
## 26    WY   hosp_ped      794      795        1 0.001258653
## 27    WA   hosp_ped    11473    11486       13 0.001132454
## 28    LA   hosp_ped    10021    10032       11 0.001097093
## 29    NH hosp_adult    90770    90907      137 0.001508171
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 41,591
## Columns: 117
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 31,512
## Columns: 82
## $ date                                   <date> 2022-04-15, 2022-04-15, 2022-0~
## $ MMWR_week                              <dbl> 15, 15, 15, 15, 15, 15, 15, 15,~
## $ state                                  <chr> "NV", "SC", "NE", "ND", "CA", "~
## $ Distributed                            <dbl> 5870110, 10352975, 3914910, 136~
## $ Distributed_Janssen                    <dbl> 258700, 451200, 150100, 52800, ~
## $ Distributed_Moderna                    <dbl> 2014400, 4306940, 1372780, 5258~
## $ Distributed_Pfizer                     <dbl> 3597010, 5594835, 2392030, 7883~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 190578, 201079, 202383, 179382,~
## $ Distributed_Per_100k_12Plus            <dbl> 223896, 234232, 242048, 214517,~
## $ Distributed_Per_100k_18Plus            <dbl> 245867, 256418, 268451, 234924,~
## $ Distributed_Per_100k_65Plus            <dbl> 1183560, 1104880, 1252940, 1140~
## $ vxa                                    <dbl> 4846899, 7387622, 3169859, 1070~
## $ Administered_12Plus                    <dbl> 4729850, 7215781, 3059482, 1035~
## $ Administered_18Plus                    <dbl> 4435348, 6828252, 2853809, 9829~
## $ Administered_65Plus                    <dbl> 1192563, 2256911, 847795, 29325~
## $ Administered_Janssen                   <dbl> 187265, 231115, 95046, 39681, 2~
## $ Administered_Moderna                   <dbl> 1659796, 2864880, 1138222, 4068~
## $ Administered_Pfizer                    <dbl> 2999310, 4289420, 1930052, 6234~
## $ Administered_Unk_Manuf                 <dbl> 528, 2207, 6539, 325, 15816, 47~
## $ Admin_Per_100k                         <dbl> 157359, 143485, 163867, 140451,~
## $ Admin_Per_100k_12Plus                  <dbl> 180405, 163254, 189160, 162456,~
## $ Admin_Per_100k_18Plus                  <dbl> 185772, 169119, 195690, 168926,~
## $ Admin_Per_100k_65Plus                  <dbl> 240450, 240860, 271331, 244697,~
## $ Recip_Administered                     <dbl> 4818924, 7391493, 3182205, 1049~
## $ Administered_Dose1_Recip               <dbl> 2308282, 3462294, 1356028, 4934~
## $ Administered_Dose1_Pop_Pct             <dbl> 74.9, 67.2, 70.1, 64.8, 83.2, 7~
## $ Administered_Dose1_Recip_12Plus        <dbl> 2242948, 3363978, 1296952, 4757~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 85.5, 76.1, 80.2, 74.7, 93.2, 8~
## $ Administered_Dose1_Recip_18Plus        <dbl> 2090215, 3165363, 1200872, 4491~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 87.5, 78.4, 82.3, 77.2, 94.2, 8~
## $ Administered_Dose1_Recip_65Plus        <dbl> 502147, 950807, 308194, 120897,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc                                    <dbl> 1864022, 2914901, 1225968, 4170~
## $ vxcpoppct                              <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 6~
## $ Series_Complete_12Plus                 <dbl> 1813258, 2838179, 1175197, 4028~
## $ Series_Complete_12PlusPop_Pct          <dbl> 69.2, 64.2, 72.7, 63.2, 80.5, 7~
## $ vxcgte18                               <dbl> 1694297, 2670106, 1088392, 3803~
## $ vxcgte18pct                            <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 7~
## $ vxcgte65                               <dbl> 415049, 810448, 286617, 102377,~
## $ vxcgte65pct                            <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 9~
## $ Series_Complete_Janssen                <dbl> 172318, 207380, 88579, 36537, 2~
## $ Series_Complete_Moderna                <dbl> 602890, 1039279, 415073, 145324~
## $ Series_Complete_Pfizer                 <dbl> 1088750, 1667852, 720640, 23513~
## $ Series_Complete_Unk_Manuf              <dbl> 64, 390, 1676, 13, 5079, 1680, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 172314, 207331, 88553, 36530, 2~
## $ Series_Complete_Moderna_12Plus         <dbl> 602884, 1039114, 415031, 145318~
## $ Series_Complete_Pfizer_12Plus          <dbl> 1037996, 1591346, 669959, 22103~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 64, 388, 1654, 13, 5014, 1656, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 172270, 206747, 88485, 36382, 2~
## $ Series_Complete_Moderna_18Plus         <dbl> 602778, 1037101, 414851, 145110~
## $ Series_Complete_Pfizer_18Plus          <dbl> 919188, 1425877, 583491, 198891~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 61, 381, 1565, 12, 4699, 1605, ~
## $ Series_Complete_Janssen_65Plus         <dbl> 26151, 31325, 6998, 4376, 20284~
## $ Series_Complete_Moderna_65Plus         <dbl> 190706, 345825, 139225, 48336, ~
## $ Series_Complete_Pfizer_65Plus          <dbl> 198156, 433078, 139467, 49660, ~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 36, 220, 927, 5, 1493, 392, 362~
## $ Additional_Doses                       <dbl> 713544, 1141995, 617137, 168496~
## $ Additional_Doses_Vax_Pct               <dbl> 38.3, 39.2, 50.3, 40.4, 50.2, 5~
## $ Additional_Doses_12Plus                <dbl> 713502, 1141860, 616974, 168479~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 39.3, 40.2, 52.5, 41.8, 52.5, 6~
## $ Additional_Doses_18Plus                <dbl> 694360, 1116070, 594777, 166705~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 41.0, 41.8, 54.6, 43.8, 54.7, 6~
## $ Additional_Doses_50Plus                <dbl> 458721, 822790, 381496, 114424,~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 53.8, 53.4, 68.1, 57.9, 65.5, 7~
## $ Additional_Doses_65Plus                <dbl> 259411, 507047, 220977, 69677, ~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 62.5, 62.6, 77.1, 68.1, 72.7, 8~
## $ Additional_Doses_Moderna               <dbl> 292543, 489262, 242700, 77363, ~
## $ Additional_Doses_Pfizer                <dbl> 408783, 629984, 367091, 88613, ~
## $ Additional_Doses_Janssen               <dbl> 12214, 22236, 6918, 2511, 22302~
## $ Additional_Doses_Unk_Manuf             <dbl> 4, 513, 428, 9, 575, 418, 61, 9~
## $ Administered_Dose1_Recip_5Plus         <dbl> 2308180, 3460120, 1355714, 4931~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 79.7, 71.3, 75.2, 69.7, 88.5, 7~
## $ Series_Complete_5Plus                  <dbl> 1864007, 2913784, 1225903, 4168~
## $ Series_Complete_5PlusPop_Pct           <dbl> 64.4, 60.0, 68.0, 58.9, 76.2, 7~
## $ Administered_5Plus                     <dbl> 4846790, 7384273, 3169464, 1069~
## $ Admin_Per_100k_5Plus                   <dbl> 167444, 152057, 175737, 151118,~
## $ Distributed_Per_100k_5Plus             <dbl> 202797, 213189, 217070, 193090,~
## $ Series_Complete_Moderna_5Plus          <dbl> 602885, 1039202, 415051, 145320~
## $ Series_Complete_Pfizer_5Plus           <dbl> 1088743, 1666860, 720616, 23500~
## $ Series_Complete_Janssen_5Plus          <dbl> 172315, 207334, 88563, 36532, 2~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 64, 388, 1673, 13, 5078, 1675, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  2.28e+10    3.70e+8   7.99e+7 968928       48026    
## 2 after   2.26e+10    3.68e+8   7.93e+7 964235       41514    
## 3 pctchg  5.25e- 3    4.38e-3   7.20e-3      0.00484     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 41,514
## Columns: 6
## $ date       <date> 2022-01-14, 2020-08-22, 2020-06-05, 2021-05-22, 2021-08-01~
## $ state      <chr> "KS", "AR", "HI", "MA", "GA", "OK", "OK", "GA", "GA", "TX",~
## $ tot_cases  <dbl> 621273, 56199, 661, 704796, 1187107, 475578, 1034439, 14780~
## $ tot_deaths <dbl> 7162, 674, 17, 17818, 21690, 7488, 14010, 3176, 1758, 49521~
## $ new_cases  <dbl> 19414, 547, 8, 451, 3829, 1028, 0, 2766, 687, 1199, 0, 29, ~
## $ new_deaths <dbl> 21, 11, 0, 5, 7, 8, 0, 3, 69, 34, 0, 0, 31, 2, 15, 7, 0, 1,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.65e+7    4.01e+7 1012359      41591     
## 2 after  4.63e+7    3.99e+7  994497      39837     
## 3 pctchg 4.78e-3    4.57e-3       0.0176     0.0422
## 
## 
## Processed for cdcHosp:
## Rows: 39,837
## Columns: 5
## $ date       <date> 2020-10-18, 2020-10-17, 2020-10-13, 2020-10-12, 2020-10-08~
## $ state      <chr> "VT", "VT", "NH", "ID", "ND", "ID", "NE", "MS", "DC", "HI",~
## $ inp        <dbl> 2, 3, 34, 221, 218, 191, 316, 516, 156, 123, 198, 116, 102,~
## $ hosp_adult <dbl> 2, 3, 34, 219, 212, 189, 315, 462, 141, 122, 193, 109, 101,~
## $ hosp_ped   <dbl> 0, 0, 0, 2, 6, 2, 6, 4, 15, 1, 5, 3, 1, 1, 0, 0, 1, 6, 32, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 3.28e+11 1.37e+11 1219443.    3.57e+10 1848256.    1.28e+11 1448866.   
## 2 after  1.58e+11 6.64e+10 1022671.    1.73e+10 1645640     6.19e+10 1227708.   
## 3 pctchg 5.19e- 1 5.16e- 1       0.161 5.16e- 1       0.110 5.17e- 1       0.153
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 24,888
## Columns: 9
## $ date        <date> 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-1~
## $ state       <chr> "NV", "SC", "NE", "ND", "CA", "MN", "DE", "WA", "AK", "CT"~
## $ vxa         <dbl> 4846899, 7387622, 3169859, 1070327, 73947936, 10165098, 17~
## $ vxc         <dbl> 1864022, 2914901, 1225968, 417012, 28314115, 3888695, 6696~
## $ vxcpoppct   <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 69.0, 68.8, 72.3, 62.0, 78.8~
## $ vxcgte65    <dbl> 415049, 810448, 286617, 102377, 5246870, 882521, 180253, 1~
## $ vxcgte65pct <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 95.0, 95.0, 93.8, 85.9, 95.0~
## $ vxcgte18    <dbl> 1694297, 2670106, 1088392, 380395, 24878761, 3415346, 6047~
## $ vxcgte18pct <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 78.8, 78.5, 82.4, 73.0, 87.7~
## 
## Integrated per capita data file:
## Rows: 41,727
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220416, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220416 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220416.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 439,873
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         8246
## 2 Critical Access Hospitals 117679
## 3 Long Term                  30218
## 4 Short Term                283730
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       33
## 2 GU      176
## 3 MP       88
## 4 PR     4824
## 5 VI      176
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        27909   411160             804 439873
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   400911           35644 439873
## 3 icu_beds_used_7_day_avg                   1649   385635           52589 439873
## 4 inpatient_beds_7_day_avg                  1730   436407            1736 439873
## 5 staffed_icu_adult_patients_confirmed_an~  4241   306239          129393 439873
## 6 total_adult_patients_hospitalized_confi~  2362   304424          133087 439873
## 7 total_beds_7_day_avg                     22106   417354             413 439873
## 8 total_icu_beds_7_day_avg                  2064   415848           21961 439873
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220416, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220416 <- postProcessCDCDaily(cdc_daily_220416, 
                                              dataThruLabel="Mar 2022", 
                                              keyDatesBurden=c("2022-03-31", "2021-09-30", 
                                                               "2021-03-31", "2020-09-30"
                                                               ),
                                              keyDatesVaccine=c("2022-03-31", "2021-11-30", 
                                                                "2021-07-31", "2021-03-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220416 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220416, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

Peaks and valleys of key metrics are also plotted:

peakValleyCDCDaily(cdc_daily_220416)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 6,012 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 6,002 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity maps with imputed capacity are created:

modStateHosp_20220416 <- skinnyHHS(indivHosp_20220416) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220416, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to mid-April 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to mid-April 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

The latest data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220501.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220501.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220501.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220416")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220416")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220416")$dfRaw$vax
                    )

cdc_daily_220501 <- readRunCDCDaily(thruLabel="Apr 30, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-04-14 new_deaths      555      747      192 0.29493088
## 2  2022-04-10 new_deaths       64       49       15 0.26548673
## 3  2022-04-12 new_deaths      430      396       34 0.08232446
## 4  2022-04-13 new_deaths      596      641       45 0.07275667
## 5  2022-04-03 new_deaths      101       94        7 0.07179487
## 6  2022-04-11 new_deaths      324      303       21 0.06698565
## 7  2022-04-09 new_deaths      150      141        9 0.06185567
## 8  2022-04-02 new_deaths      162      154        8 0.05063291
## 9  2022-04-09  new_cases    14426    13247     1179 0.08520941
## 10 2022-04-13  new_cases    47095    51168     4073 0.08289997
## 11 2022-04-10  new_cases    18655    17702      953 0.05242457

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     KY tot_deaths   5084815   5070287    14528 0.002861222
## 2     CO  tot_cases 373235234 372250769   984465 0.002641136
## 3     KY new_deaths     15445     15251      194 0.012640083
## 4     NV new_deaths     10223     10340      117 0.011379663
## 5     AL new_deaths     19552     19502       50 0.002560557
## 6     CO new_deaths     12001     12031       30 0.002496671
## 7     FL new_deaths     73846     73689      157 0.002128309
## 8     SC new_deaths     17733     17698       35 0.001975671
## 9     NC new_deaths     23362     23334       28 0.001199246
## 10    CO  new_cases   1373102   1361600    11502 0.008411885
## 11    NC  new_cases   2643272   2639241     4031 0.001526168
## 12    KY  new_cases   1323254   1321450     1804 0.001364236
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 49,740
## Columns: 15
## $ date           <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state          <chr> "KS", "AS", "AR", "MP", "AS", "CO", "MA", "PR", "GA", "~
## $ tot_cases      <dbl> 621273, 11, 56199, 37, 0, 944337, 704796, 35112, 118710~
## $ conf_cases     <dbl> 470516, NA, NA, 37, NA, 862950, 659246, 34791, 937515, ~
## $ prob_cases     <dbl> 150757, NA, NA, 0, NA, 81387, 45550, 321, 249592, 94752~
## $ new_cases      <dbl> 19414, 0, 547, 1, 0, 10817, 451, 619, 3829, 203, 0, 175~
## $ pnew_case      <dbl> 6964, 0, 0, 0, 0, 931, 46, 1, 1144, 54, 0, 168, 317, 0,~
## $ tot_deaths     <dbl> 7162, 0, 674, 2, 0, 10271, 17818, 805, 21690, 7256, 0, ~
## $ conf_death     <dbl> NA, NA, NA, 2, NA, 9089, 17458, 624, 18725, 6176, 0, 28~
## $ prob_death     <dbl> NA, NA, NA, 0, NA, 1182, 360, 181, 2965, 1080, 0, 5188,~
## $ new_deaths     <dbl> 21, 0, 11, 0, 0, 31, 5, 3, 7, 0, 0, 20, 3, 0, 69, 34, 0~
## $ pnew_death     <dbl> 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, -7, 0, 0, 0, 0, NA, 0,~
## $ created_at     <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases  <chr> "Agree", NA, "Not agree", "Agree", NA, "Agree", "Agree"~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Agree", "Agree", ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta   pctDelta
## 1 2020-07-25 hosp_ped     4621     4270      351 0.07895625

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     AS        inp      354      356        2 0.005633803
## 2     VI        inp     4288     4296        8 0.001863933
## 3     NH   hosp_ped      898      944       46 0.049945711
## 4     ME   hosp_ped     1889     1945       56 0.029212311
## 5     WV   hosp_ped     5138     5034      104 0.020448289
## 6     AR   hosp_ped    11326    11212      114 0.010116248
## 7     ID   hosp_ped     3585     3550       35 0.009810792
## 8     AL   hosp_ped    18802    18972      170 0.009000900
## 9     IN   hosp_ped    16332    16230      102 0.006264971
## 10    NJ   hosp_ped    16929    17023       94 0.005537229
## 11    MO   hosp_ped    35405    35251      154 0.004359149
## 12    PR   hosp_ped    17701    17774       73 0.004115574
## 13    CO   hosp_ped    19247    19322       75 0.003889134
## 14    TN   hosp_ped    20056    20133       77 0.003831894
## 15    NM   hosp_ped     6922     6896       26 0.003763207
## 16    VA   hosp_ped    15517    15465       52 0.003356788
## 17    UT   hosp_ped     8779     8750       29 0.003308803
## 18    AK   hosp_ped     2227     2233        6 0.002690583
## 19    MD   hosp_ped    13808    13771       37 0.002683201
## 20    MS   hosp_ped     9988    10011       23 0.002300115
## 21    KY   hosp_ped    17263    17298       35 0.002025404
## 22    GA   hosp_ped    46480    46386       94 0.002024422
## 23    CA   hosp_ped    70500    70639      139 0.001969689
## 24    FL   hosp_ped    85064    84922      142 0.001670726
## 25    IA   hosp_ped     6846     6835       11 0.001608070
## 26    CT   hosp_ped     6956     6966       10 0.001436575
## 27    SC   hosp_ped     8214     8204       10 0.001218175
## 28    TX   hosp_ped   103805   103689      116 0.001118105
## 29    NV   hosp_ped     4552     4547        5 0.001099022
## 30    AS hosp_adult      350      352        2 0.005698006
## 31    VI hosp_adult     4039     4047        8 0.001978729
## 32    NH hosp_adult    92783    92686       97 0.001045997
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 42,401
## Columns: 135
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> ~
## $ all_pediatric_inpatient_beds                                                 <dbl> ~
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> ~
## $ total_staffed_pediatric_icu_beds                                             <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 32,472
## Columns: 82
## $ date                                   <date> 2022-04-30, 2022-04-30, 2022-0~
## $ MMWR_week                              <dbl> 17, 17, 17, 17, 17, 17, 17, 17,~
## $ state                                  <chr> "IN", "NM", "US", "ME", "KY", "~
## $ Distributed                            <dbl> 13462080, 4474445, 728344715, 3~
## $ Distributed_Janssen                    <dbl> 607200, 187600, 30749100, 15400~
## $ Distributed_Moderna                    <dbl> 4734100, 1748900, 270415980, 13~
## $ Distributed_Pfizer                     <dbl> 8120780, 2537945, 427179635, 19~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 199965, 213391, 219375, 253996,~
## $ Distributed_Per_100k_12Plus            <dbl> 235968, 250328, 256893, 288138,~
## $ Distributed_Per_100k_18Plus            <dbl> 260679, 276031, 282020, 311698,~
## $ Distributed_Per_100k_65Plus            <dbl> 1239900, 1184950, 1329290, 1196~
## $ vxa                                    <dbl> 9524604, 3935871, 575765730, 28~
## $ Administered_12Plus                    <dbl> 9276622, 3800236, 557160223, 27~
## $ Administered_18Plus                    <dbl> 8736719, 3533433, 520923954, 26~
## $ Administered_65Plus                    <dbl> 2742875, 1042375, 146082778, 87~
## $ Administered_Janssen                   <dbl> 305604, 119590, 18703265, 14369~
## $ Administered_Moderna                   <dbl> 3431369, 1598596, 216827616, 11~
## $ Administered_Pfizer                    <dbl> 5756128, 2208144, 339679887, 15~
## $ Administered_Unk_Manuf                 <dbl> 31503, 9541, 554962, 3615, 2531~
## $ Admin_Per_100k                         <dbl> 141478, 187706, 173419, 211476,~
## $ Admin_Per_100k_12Plus                  <dbl> 162604, 212609, 196515, 232502,~
## $ Admin_Per_100k_18Plus                  <dbl> 169177, 217980, 201705, 237494,~
## $ Admin_Per_100k_65Plus                  <dbl> 252627, 276048, 266613, 307600,~
## $ Recip_Administered                     <dbl> 9536900, 4083307, 575765730, 28~
## $ Administered_Dose1_Recip               <dbl> 4135482, 1837743, 257641065, 12~
## $ Administered_Dose1_Pop_Pct             <dbl> 61.4, 87.6, 77.6, 90.4, 66.1, 8~
## $ Administered_Dose1_Recip_12Plus        <dbl> 3990122, 1755545, 247398872, 11~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 69.9, 95.0, 87.3, 95.0, 75.2, 9~
## $ Administered_Dose1_Recip_18Plus        <dbl> 3731041, 1618184, 229910032, 11~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 72.2, 95.0, 89.0, 95.0, 77.6, 9~
## $ Administered_Dose1_Recip_65Plus        <dbl> 1009378, 428113, 56761008, 3298~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 93.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc                                    <dbl> 3690380, 1490068, 219675939, 10~
## $ vxcpoppct                              <dbl> 54.8, 71.1, 66.2, 79.5, 57.4, 6~
## $ Series_Complete_12Plus                 <dbl> 3588448, 1428092, 211424055, 10~
## $ Series_Complete_12PlusPop_Pct          <dbl> 62.9, 79.9, 74.6, 86.7, 65.4, 7~
## $ vxcgte18                               <dbl> 3366149, 1314314, 196498734, 96~
## $ vxcgte18pct                            <dbl> 65.2, 81.1, 76.1, 88.2, 67.5, 7~
## $ vxcgte65                               <dbl> 943206, 356210, 49434951, 28625~
## $ vxcgte65pct                            <dbl> 86.9, 94.3, 90.2, 95.0, 86.3, 9~
## $ Series_Complete_Janssen                <dbl> 281574, 109986, 16953131, 13183~
## $ Series_Complete_Moderna                <dbl> 1254015, 563214, 76433248, 3852~
## $ Series_Complete_Pfizer                 <dbl> 2146277, 814554, 126132484, 551~
## $ Series_Complete_Unk_Manuf              <dbl> 8514, 2314, 157076, 845, 1838, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 281541, 109967, 16948031, 13180~
## $ Series_Complete_Moderna_12Plus         <dbl> 1253947, 563150, 76426770, 3852~
## $ Series_Complete_Pfizer_12Plus          <dbl> 2044486, 752672, 117894600, 510~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 8474, 2303, 154654, 837, 1819, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 281258, 109824, 16921288, 13174~
## $ Series_Complete_Moderna_18Plus         <dbl> 1253587, 562729, 76344491, 3851~
## $ Series_Complete_Pfizer_18Plus          <dbl> 1822963, 639497, 103084542, 448~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 8341, 2264, 148413, 755, 1713, ~
## $ Series_Complete_Janssen_65Plus         <dbl> 31120, 21383, 2360115, 24650, 3~
## $ Series_Complete_Moderna_65Plus         <dbl> 461227, 167308, 23565506, 13124~
## $ Series_Complete_Pfizer_65Plus          <dbl> 446929, 166389, 23443158, 13006~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 3930, 1130, 66172, 294, 846, 36~
## $ Additional_Doses                       <dbl> 1693251, 739576, 100600067, 597~
## $ Additional_Doses_Vax_Pct               <dbl> 45.9, 49.6, 45.8, 55.9, 44.2, 4~
## $ Additional_Doses_12Plus                <dbl> 1689014, 739365, 100571760, 597~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 47.1, 51.8, 47.6, 58.1, 45.5, 4~
## $ Additional_Doses_18Plus                <dbl> 1636009, 706410, 96911167, 5754~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 48.6, 53.7, 49.3, 59.6, 47.2, 4~
## $ Additional_Doses_50Plus                <dbl> 1114515, 442723, 61293070, 3915~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 59.7, 63.5, 60.7, 70.1, 59.1, 6~
## $ Additional_Doses_65Plus                <dbl> 652191, 246351, 33899976, 22276~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 69.1, 69.2, 68.6, 77.8, 68.5, 6~
## $ Additional_Doses_Moderna               <dbl> 659070, 310369, 43251133, 27256~
## $ Additional_Doses_Pfizer                <dbl> 1011148, 418139, 55814930, 3131~
## $ Additional_Doses_Janssen               <dbl> 21456, 10800, 1502478, 10855, 2~
## $ Additional_Doses_Unk_Manuf             <dbl> 1577, 268, 31526, 622, 220, 59,~
## $ Administered_Dose1_Recip_5Plus         <dbl> 4135027, 1837487, 257532058, 12~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 65.5, 93.0, 82.5, 94.9, 70.4, 8~
## $ Series_Complete_5Plus                  <dbl> 3690294, 1490006, 219626527, 10~
## $ Series_Complete_5PlusPop_Pct           <dbl> 58.4, 75.4, 70.3, 83.5, 61.1, 7~
## $ Administered_5Plus                     <dbl> 9524148, 3935594, 575608906, 28~
## $ Admin_Per_100k_5Plus                   <dbl> 150845, 199186, 184333, 221953,~
## $ Distributed_Per_100k_5Plus             <dbl> 213214, 226458, 233246, 266598,~
## $ Series_Complete_Moderna_5Plus          <dbl> 1254006, 563197, 76430070, 3852~
## $ Series_Complete_Pfizer_5Plus           <dbl> 2146218, 814520, 126089538, 551~
## $ Series_Complete_Janssen_5Plus          <dbl> 281557, 109976, 16950044, 13181~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 8513, 2313, 156875, 845, 1837, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  2.40e+10    3.85e+8   8.07e+7 974312       48911    
## 2 after   2.39e+10    3.83e+8   8.00e+7 969594       42279    
## 3 pctchg  5.35e- 3    4.40e-3   7.66e-3      0.00484     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 42,279
## Columns: 6
## $ date       <date> 2022-01-14, 2020-08-22, 2021-12-31, 2021-05-22, 2021-08-01~
## $ state      <chr> "KS", "AR", "CO", "MA", "GA", "OK", "GA", "GA", "TX", "AK",~
## $ tot_cases  <dbl> 621273, 56199, 944337, 704796, 1187107, 449170, 147804, 383~
## $ tot_deaths <dbl> 7162, 674, 10271, 17818, 21690, 7256, 3176, 1758, 49521, 9,~
## $ new_cases  <dbl> 19414, 547, 10817, 451, 3829, 203, 2766, 687, 1199, 7, 1723~
## $ new_deaths <dbl> 21, 11, 31, 5, 7, 0, 3, 69, 34, 0, 127, 31, 0, 9, 15, 7, 0,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.68e+7    4.03e+7 1027403      42401     
## 2 after  4.66e+7    4.01e+7 1008991      40602     
## 3 pctchg 4.82e-3    4.60e-3       0.0179     0.0424
## 
## 
## Processed for cdcHosp:
## Rows: 40,602
## Columns: 5
## $ date       <date> 2020-10-13, 2020-10-12, 2020-10-09, 2020-10-04, 2020-09-22~
## $ state      <chr> "RI", "VT", "RI", "RI", "VT", "RI", "AK", "RI", "DE", "VT",~
## $ inp        <dbl> 124, 0, 116, 92, 1, 85, 50, 75, 89, 6, 12, 160, 69, 33, 70,~
## $ hosp_adult <dbl> 123, 0, 115, 91, 1, 84, 49, 75, 84, 5, 9, 115, 66, 33, 55, ~
## $ hosp_ped   <dbl> 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 3, 46, 0, 0, 15, 147, 0, 1, 0~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 3.45e+11 1.44e+11 1278961.    3.72e+10 1927715.    1.34e+11 1516669.   
## 2 after  1.66e+11 6.96e+10 1072129.    1.80e+10 1714305.    6.48e+10 1284690.   
## 3 pctchg 5.19e- 1 5.16e- 1       0.162 5.16e- 1       0.111 5.16e- 1       0.153
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 25,653
## Columns: 9
## $ date        <date> 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-3~
## $ state       <chr> "IN", "NM", "ME", "KY", "DE", "MA", "CA", "TX", "WA", "SC"~
## $ vxa         <dbl> 9524604, 3935871, 2842688, 6522218, 1813671, 14845867, 750~
## $ vxc         <dbl> 3690380, 1490068, 1069169, 2563070, 672952, 5441750, 28464~
## $ vxcpoppct   <dbl> 54.8, 71.1, 79.5, 57.4, 69.1, 79.0, 72.0, 61.4, 72.6, 56.7~
## $ vxcgte65    <dbl> 943206, 356210, 286250, 647476, 181583, 1124456, 5280949, ~
## $ vxcgte65pct <dbl> 86.9, 94.3, 95.0, 86.3, 95.0, 95.0, 90.5, 87.3, 94.2, 86.6~
## $ vxcgte18    <dbl> 3366149, 1314314, 965876, 2338367, 607641, 4805717, 249983~
## $ vxcgte18pct <dbl> 65.2, 81.1, 88.2, 67.5, 78.9, 86.8, 81.6, 72.5, 82.6, 66.2~
## 
## Integrated per capita data file:
## Rows: 42,492
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220501, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220501 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220501.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 449,805
## Columns: 109
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         8432
## 2 Critical Access Hospitals 120273
## 3 Long Term                  30899
## 4 Short Term                290201
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       35
## 2 GU      180
## 3 MP       90
## 4 PR     4930
## 5 VI      180
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        32098   416881             826 449805
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   409902           36585 449805
## 3 icu_beds_used_7_day_avg                   1650   394165           53990 449805
## 4 inpatient_beds_7_day_avg                  1728   446299            1778 449805
## 5 staffed_icu_adult_patients_confirmed_an~  4238   313313          132254 449805
## 6 total_adult_patients_hospitalized_confi~  2359   310541          136905 449805
## 7 total_beds_7_day_avg                     26089   423289             427 449805
## 8 total_icu_beds_7_day_avg                  2065   425219           22521 449805
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220501, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501, 
                                              dataThruLabel="Apr 2022", 
                                              keyDatesBurden=c("2022-04-29", "2021-10-31", 
                                                               "2021-04-30", "2020-10-31"
                                                               ),
                                              keyDatesVaccine=c("2022-04-29", "2021-12-31", 
                                                                "2021-08-31", "2021-04-30"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220501, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

The post-process function is working incorrectly on the pediatric data. It appears that a few category labels changed syntax. Function createBurdenPivot() is updated to a better formed grep sequence for the latest data:

burdenPivotList_220501$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n())
## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can
## override using the `.groups` argument.
## # A tibble: 19 × 6
## # Groups:   adultPed, confSusp, age [18]
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con… 4.20e4 42401
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 2.49e5 42401
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 3.65e5 42401
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 4.55e5 42401
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 7.25e5 42401
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 9.29e5 42401
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 9.05e5 42401
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 7.73e5 42401
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus… 3.35e4 42401
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 2.27e5 42401
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 2.97e5 42401
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 3.03e5 42401
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 4.79e5 42401
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 6.54e5 42401
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 6.32e5 42401
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 5.75e5 42401
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 1.30e5 42401
## 18 ped      suspected 0-19  all_pediatric_inpatient_bed_occupied    2.00e7 42401
## 19 ped      suspected 0-19  previous_day_admission_pediatric_covid… 3.19e5 42401
# Create pivoted burden data
createBurdenPivot <- function(lst, 
                              dataThru,
                              minDatePlot="2020-08-01", 
                              plotByState=c(state.abb, "DC")
                              ) {
    
    # FUNCTION ARGUMENTS:
    # lst: a processed list that includes sub-component $dfRaw$cdcHosp
    # dataThru: character string to be used for 'data through'; most commonly MMM-YY
    # minDatePlot: starting date for plots
    # plotByState: states to be facetted for plot of hospitaliztions by age (FALSE means do not create plot)
    
    # Convert minDatePlot to Date if passed as character
    if ("character" %in% class(minDatePlot)) minDatePlot <- as.Date(minDatePlot)
    
    # Create the hospitalized by age data
    hospAge <- lst[["dfRaw"]][["cdcHosp"]] %>%
        select(state, 
               date, 
               grep(x=names(.), pattern="previous_.*ed_\\d.*[9+]$", value=TRUE), 
               grep(x=names(.), pattern="previous_.*pediatric.*[tm]ed$", value=TRUE)
        ) %>% 
        pivot_longer(-c(state, date)) %>% 
        mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"), 
               adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"), 
               age=ifelse(adultPed=="ped", 
                          "0-17", 
                          stringr::str_replace_all(string=name, pattern=".*_", replacement="")
               ), 
               age=ifelse(age %in% c("0-17", "18-19"), "0-19", age), 
               div=as.character(state.division)[match(state, state.abb)]
        )
    
    # Create the pivoted burden data
    dfPivot <- makeCaseHospDeath(dfHosp=hospAge, dfCaseDeath=lst[["dfPerCapita"]])
    
    # Plot for overall trends by age group
    p1 <- hospAge %>% 
        filter(state %in% c(state.abb, "DC"), !is.na(value)) %>% 
        mutate(ageBucket=age) %>% 
        group_by(date, ageBucket) %>% 
        summarize(value=sum(value), .groups="drop") %>% 
        arrange(date) %>%
        group_by(ageBucket) %>% 
        mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>% 
        filter(date >= minDatePlot) %>% 
        ggplot(aes(x=date, y=value7)) + 
        labs(x=NULL, 
             y="Confirmed or suspected COVID admissions (rolling-7 mean)", 
             title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")"), 
             subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
        ) + 
        lims(y=c(0, NA))
    
    # Create three main plots of hospitalized by age data
    print(p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) + scale_color_discrete("Age\nbucket"))
    print(p1 + geom_col(aes(fill=ageBucket), position="stack") + scale_color_discrete("Age\nbucket"))
    print(p1 + geom_col(aes(fill=ageBucket), position="fill") + scale_color_discrete("Age\nbucket"))
    
    # Plot for trends by state and age group
    if (!isFALSE(plotByState)) {
        p2 <- hospAge %>% 
            filter(state %in% plotByState, !is.na(value)) %>% 
            mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>% 
            group_by(date, state, ageBucket) %>% 
            summarize(value=sum(value), .groups="drop") %>% 
            group_by(ageBucket, state) %>% 
            mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>% 
            filter(date >= minDatePlot) %>% 
            ggplot(aes(x=date, y=value7)) + 
            geom_line(aes(color=ageBucket, group=ageBucket)) + 
            scale_color_discrete("Age\nbucket") + 
            labs(x=NULL, 
                 y="Confirmed or suspected COVID admissions (rolling-7 mean)", 
                 title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")")
            ) + 
            lims(y=c(0, NA)) + 
            facet_wrap(~state, scales="free_y")
        print(p2)
    }
    
    # Return key data (do not return plot objects)
    list(hospAge=hospAge, dfPivot=dfPivot)
    
}

# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501, 
                                              dataThruLabel="Apr 2022", 
                                              keyDatesBurden=c("2022-04-29", "2021-10-31", 
                                                               "2021-04-30", "2020-10-31"
                                                               ),
                                              keyDatesVaccine=c("2022-04-29", "2021-12-31", 
                                                                "2021-08-31", "2021-04-30"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220501, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220501$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n())
## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can
## override using the `.groups` argument.
## # A tibble: 18 × 6
## # Groups:   adultPed, confSusp, age [18]
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con…  41964 42401
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 249178 42401
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 365345 42401
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 454857 42401
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 724912 42401
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 929208 42401
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 905339 42401
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 772981 42401
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus…  33526 42401
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 226590 42401
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 296759 42401
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 303443 42401
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 479067 42401
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 653801 42401
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 632370 42401
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 574885 42401
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 130296 42401
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid… 318942 42401

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220501)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 6,192 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 6,182 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity maps with imputed capacity are created:

modStateHosp_20220501 <- skinnyHHS(indivHosp_20220501) %>%
    imputeNACapacity() %>%
    sumImputedHHS()

# ICU summary
createGeoMap(modStateHosp_20220501, 
             yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                        "pctICU"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                           "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                           ), 
             plotTitle="Average % ICU Capacity Filled by Week", 
             plotSubtitle="August 2020 to April 2022", 
             plotScaleLabel="% ICU\nUsed", 
             returnData=FALSE
             )

# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
createGeoMap(modStateHosp_20220501 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))), 
             yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                        "pctAdult"=c("label"="Total", "color"="black")
                        ), 
             fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                           "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                           ), 
             plotTitle="Average % Adult Beds Capacity Filled by Week", 
             plotSubtitle="August 2020 to April 2022\n(AK, CT, DE, and SD data excluded)", 
             plotScaleLabel="% Adult\nBeds\nUsed", 
             returnData=FALSE
             )

A function is created for hospital post-processing:

hospitalCapacityCDCDaily <- function(df, 
                                     createData=TRUE, 
                                     returnData=createData,
                                     maxCapacity=1.2,
                                     plotSub="start to finish"
                                     ) {
    
    # FUNCTION ARGUMENTS:
    # df: the key data frame
    # createData: boolean, if TRUE then convert df for use in processing
    #                      if FALSE, use df as-is
    # returnData: boolean, should df be returned (defaults to TRUE is modified, FALSE otherwise)
    # maxCapacity: states that exceed this capacity level will not be plotted (explore separately)
    # plotSub: subtitle to use for plots
    
    # Convert data if requested
    if(isTRUE(createData)) df <- skinnyHHS(df) %>% imputeNACapacity() %>% sumImputedHHS()

    # Create ICU summary
    createGeoMap(df, 
                 yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"), 
                            "pctICU"=c("label"="Total", "color"="black")
                            ), 
                 fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds), 
                               "pctCovidICU"=expression(adult_icu_covid/icu_beds)
                               ), 
                 plotTitle="Average % ICU Capacity Filled by Week", 
                 plotSubtitle=plotSub, 
                 plotScaleLabel="% ICU\nUsed", 
                 returnData=FALSE
                 )

    # Get list of states that may complicate map
    pctState <- df %>% 
        mutate(pctAdult=adult_beds_occupied/adult_beds, pctCovidAdult=adult_beds_covid/adult_beds)
    exclStates <- pctState %>% filter(pctAdult > maxCapacity) %>% count(state) %>% pull(state)
    if(length(exclStates) > 0) plotSub <- paste0(plotSub, "\n(", paste(exclStates, collapse=", "), " data excluded)")

    # Create the adult beds summary    
    createGeoMap(df %>% filter(!(state %in% all_of(exclStates))), 
                 yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"), 
                            "pctAdult"=c("label"="Total", "color"="black")
                            ), 
                 fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds), 
                               "pctCovidAdult"=expression(adult_beds_covid/adult_beds)
                               ), 
                 plotTitle="Average % Adult Beds Capacity Filled by Week", 
                 plotSubtitle=plotSub, 
                 plotScaleLabel="% Adult\nBeds\nUsed", 
                 returnData=FALSE
                 )
    
    # Return the data if requested (defaults to only if createData is TRUE)
    if(isTRUE(returnData)) return(df)
    
}

hospitalCapacityCDCDaily(modStateHosp_20220501, createData=FALSE, plotSub="August 2020 to April 2022")

The latest data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220602.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220602.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220602.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220501")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220501")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220501")$dfRaw$vax
                    )

cdc_daily_220602 <- readRunCDCDaily(thruLabel="May 31, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-04-24 new_deaths       56       40       16 0.33333333
## 2  2022-04-16 new_deaths       40       31        9 0.25352113
## 3  2021-07-30 new_deaths      522      651      129 0.21994885
## 4  2020-06-22 new_deaths      431      496       65 0.14023732
## 5  2022-04-02 new_deaths      143      162       19 0.12459016
## 6  2022-03-20 new_deaths      147      134       13 0.09252669
## 7  2022-04-03 new_deaths      110      101        9 0.08530806
## 8  2022-03-12 new_deaths      505      548       43 0.08167142
## 9  2022-03-05 new_deaths      322      346       24 0.07185629
## 10 2022-04-25 new_deaths      195      182       13 0.06896552
## 11 2022-03-27 new_deaths       91       85        6 0.06818182
## 12 2022-04-26 new_deaths      355      334       21 0.06095791
## 13 2022-01-02 new_deaths      380      358       22 0.05962060
## 14 2022-04-28 new_deaths      282      266       16 0.05839416
## 15 2022-04-29 new_deaths      476      503       27 0.05515832
## 16 2022-04-27 new_deaths      727      768       41 0.05484950
## 17 2022-03-19 new_deaths      196      207       11 0.05459057
## 18 2022-03-06 new_deaths      364      345       19 0.05359661
## 19 2022-04-27  new_cases    79917    88535     8618 0.10231995
## 20 2022-04-29  new_cases    86370    78910     7460 0.09027106
## 21 2022-04-23  new_cases    27418    25206     2212 0.08406811
## 22 2022-04-24  new_cases    25174    23358     1816 0.07483722

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     DE tot_deaths   1172100   1092040    80060 0.070720008
## 2     NC tot_deaths   8864991   8762468   102523 0.011632193
## 3     KY tot_deaths   5355366   5316652    38714 0.007255235
## 4     DE  tot_cases  75721383  76164588   443205 0.005836023
## 5     CO  tot_cases 394838272 393905105   933167 0.002366212
## 6     NC new_deaths     24600     23405     1195 0.049786481
## 7     KY new_deaths     15904     15523      381 0.024246667
## 8     DE new_deaths      2930      2907       23 0.007880761
## 9     AL new_deaths     19627     19570       57 0.002908386
## 10    FL new_deaths     74141     73948      193 0.002606541
## 11    CO  new_cases   1391889   1382905     8984 0.006475436
## 12    DE  new_cases    262456    263913     1457 0.005536040
## 13    NC  new_cases   2669969   2659255    10714 0.004020848
## 14    SC  new_cases   1477235   1474272     2963 0.002007788
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 51,660
## Columns: 15
## $ date           <date> 2021-03-11, 2021-02-12, 2021-08-25, 2022-05-30, 2020-0~
## $ state          <chr> "KS", "UT", "CO", "AK", "TX", "AS", "CO", "MA", "PR", "~
## $ tot_cases      <dbl> 297229, 359641, 608176, 251425, 361125, 0, 58307, 70479~
## $ conf_cases     <dbl> 241035, 359641, 562668, NA, NA, NA, 53980, 659246, 3479~
## $ prob_cases     <dbl> 56194, 0, 45508, NA, NA, NA, 4327, 45550, 321, 249592, ~
## $ new_cases      <dbl> 0, 1060, 1974, 0, 9507, 0, 223, 451, 619, 3829, 0, 0, 0~
## $ pnew_case      <dbl> 0, 0, 215, 0, 0, 0, 11, 46, 1, 1144, 0, 0, 0, 317, 5246~
## $ tot_deaths     <dbl> 4851, 1785, 7088, 1252, 7981, 0, 1944, 17818, 805, 2169~
## $ conf_death     <dbl> NA, 1729, 6282, NA, NA, NA, 1596, 17458, 624, 18725, NA~
## $ prob_death     <dbl> NA, 56, 806, NA, NA, NA, 348, 360, 181, 2965, NA, NA, 4~
## $ new_deaths     <dbl> 0, 11, 4, 0, 281, 0, 0, 5, 3, 7, 0, 0, 0, 3, 417, 7, 0,~
## $ pnew_death     <dbl> 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, NA, 0, NA~
## $ created_at     <chr> "03/12/2021 03:20:13 PM", "02/13/2021 02:50:08 PM", "08~
## $ consent_cases  <chr> "Agree", "Agree", "Agree", "N/A", "Not agree", NA, "Agr~
## $ consent_deaths <chr> "N/A", "Agree", "Agree", "N/A", "Not agree", NA, "Agree~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta  pctDelta
## 1 2020-08-02 hosp_ped     4737     4158      579 0.1301855
## 2 2022-04-30 hosp_ped      930     1042      112 0.1135903
## 3 2020-07-25 hosp_ped     4159     4621      462 0.1052392

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     VI        inp     4362     4352       10 0.002295157
## 2     NH   hosp_ped      876      922       46 0.051167964
## 3     WV   hosp_ped     5009     5185      176 0.034530116
## 4     ME   hosp_ped     1945     1995       50 0.025380711
## 5     NV   hosp_ped     4724     4630       94 0.020098354
## 6     MA   hosp_ped    10782    10657      125 0.011660992
## 7     ID   hosp_ped     3579     3615       36 0.010008340
## 8     TN   hosp_ped    20271    20100      171 0.008471428
## 9     DE   hosp_ped     4471     4504       33 0.007353760
## 10    SD   hosp_ped     4050     4077       27 0.006644518
## 11    NJ   hosp_ped    17232    17122      110 0.006403912
## 12    AL   hosp_ped    18848    18955      107 0.005660926
## 13    PR   hosp_ped    18423    18324       99 0.005388195
## 14    NM   hosp_ped     7040     7003       37 0.005269529
## 15    IN   hosp_ped    16341    16423       82 0.005005494
## 16    KS   hosp_ped     4449     4427       22 0.004957188
## 17    AR   hosp_ped    11376    11432       56 0.004910558
## 18    MS   hosp_ped    10152    10107       45 0.004442470
## 19    KY   hosp_ped    17644    17582       62 0.003520127
## 20    UT   hosp_ped     8889     8919       30 0.003369272
## 21    PA   hosp_ped    48788    48625      163 0.003346576
## 22    SC   hosp_ped     8291     8268       23 0.002777946
## 23    NC   hosp_ped    27671    27724       53 0.001913530
## 24    FL   hosp_ped    85396    85542      146 0.001708222
## 25    MO   hosp_ped    35728    35789       61 0.001705888
## 26    OK   hosp_ped    23815    23775       40 0.001681025
## 27    OR   hosp_ped     9286     9301       15 0.001614031
## 28    WA   hosp_ped    12700    12720       20 0.001573564
## 29    AZ   hosp_ped    25363    25324       39 0.001538856
## 30    HI   hosp_ped     2447     2450        3 0.001225240
## 31    CA   hosp_ped    71305    71223       82 0.001150651
## 32    TX   hosp_ped   105183   105302      119 0.001130722
## 33    VI hosp_adult     4113     4103       10 0.002434275
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 44,129
## Columns: 135
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> ~
## $ all_pediatric_inpatient_beds                                                 <dbl> ~
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> ~
## $ total_staffed_pediatric_icu_beds                                             <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: Second_Booster
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 34,520
## Columns: 83
## $ date                                   <date> 2022-06-01, 2022-06-01, 2022-0~
## $ MMWR_week                              <dbl> 22, 22, 22, 22, 22, 22, 22, 22,~
## $ state                                  <chr> "MP", "TN", "HI", "PA", "FL", "~
## $ Distributed                            <dbl> 127130, 13267730, 3582480, 3162~
## $ Distributed_Janssen                    <dbl> 3600, 517400, 124600, 1537400, ~
## $ Distributed_Moderna                    <dbl> 25720, 5085840, 1344520, 121049~
## $ Distributed_Pfizer                     <dbl> 97810, 7664490, 2113360, 179791~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 245183, 194280, 253023, 247005,~
## $ Distributed_Per_100k_12Plus            <dbl> 300615, 227360, 295802, 285353,~
## $ Distributed_Per_100k_18Plus            <dbl> 352405, 249435, 321010, 311010,~
## $ Distributed_Per_100k_65Plus            <dbl> 3386520, 1160380, 1334520, 1321~
## $ vxa                                    <dbl> 109753, 10128911, 2985746, 2350~
## $ Administered_12Plus                    <dbl> 101165, 9920484, 2874468, 22760~
## $ Administered_18Plus                    <dbl> 89008, 9438725, 2681590, 214476~
## $ Administered_65Plus                    <dbl> 8877, 3018351, 783871, 6852918,~
## $ Administered_Janssen                   <dbl> 1381, 267298, 71494, 783616, 14~
## $ Administered_Moderna                   <dbl> 15143, 3880829, 1097497, 909454~
## $ Administered_Pfizer                    <dbl> 93220, 5914777, 1816453, 136239~
## $ Administered_Unk_Manuf                 <dbl> 9, 66007, 302, 1452, 145805, 2,~
## $ Admin_Per_100k                         <dbl> 211670, 148318, 210877, 183593,~
## $ Admin_Per_100k_12Plus                  <dbl> 239217, 170000, 237342, 205388,~
## $ Admin_Per_100k_18Plus                  <dbl> 246730, 177449, 240285, 210946,~
## $ Admin_Per_100k_65Plus                  <dbl> 236468, 263982, 292001, 286330,~
## $ Recip_Administered                     <dbl> 109921, 9944936, 2998151, 23563~
## $ Administered_Dose1_Recip               <dbl> 45593, 4265547, 1243424, 109511~
## $ Administered_Dose1_Pop_Pct             <dbl> 87.9, 62.5, 87.8, 85.5, 79.7, 4~
## $ Administered_Dose1_Recip_12Plus        <dbl> 41088, 4153960, 1183344, 105535~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 71.2, 95.0, 95.0, 89.3, 5~
## $ Administered_Dose1_Recip_18Plus        <dbl> 35684, 3918229, 1094955, 991214~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 73.7, 95.0, 95.0, 91.2, 5~
## $ Administered_Dose1_Recip_65Plus        <dbl> 3247, 1059579, 272827, 2752789,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 86.5, 92.7, 95.0, 95.0, 95.0, 4~
## $ vxc                                    <dbl> 43379, 3742228, 1113175, 884697~
## $ vxcpoppct                              <dbl> 83.7, 54.8, 78.6, 69.1, 67.3, 3~
## $ Series_Complete_12Plus                 <dbl> 39312, 3649584, 1061296, 852234~
## $ Series_Complete_12PlusPop_Pct          <dbl> 93.0, 62.5, 87.6, 76.9, 75.6, 4~
## $ vxcgte18                               <dbl> 34156, 3448630, 981204, 7997796~
## $ vxcgte18pct                            <dbl> 94.7, 64.8, 87.9, 78.7, 77.3, 4~
## $ vxcgte65                               <dbl> 3124, 971113, 249781, 2297628, ~
## $ vxcgte65pct                            <dbl> 83.2, 84.9, 93.0, 95.0, 91.6, 3~
## $ Series_Complete_Janssen                <dbl> 1169, 237079, 65595, 727556, 13~
## $ Series_Complete_Moderna                <dbl> 5225, 1328160, 367035, 3165750,~
## $ Series_Complete_Pfizer                 <dbl> 36982, 2163982, 680525, 4952969~
## $ Series_Complete_Unk_Manuf              <dbl> 3, 13007, 20, 695, 41446, 3, 53~
## $ Series_Complete_Janssen_12Plus         <dbl> 1169, 237021, 65565, 727452, 13~
## $ Series_Complete_Moderna_12Plus         <dbl> 5225, 1328098, 366976, 3165432,~
## $ Series_Complete_Pfizer_12Plus          <dbl> 32915, 2071553, 628735, 4628770~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 3, 12912, 20, 692, 40906, 3, 53~
## $ Series_Complete_Janssen_18Plus         <dbl> 1169, 236756, 65381, 726950, 13~
## $ Series_Complete_Moderna_18Plus         <dbl> 5224, 1327470, 366140, 3163173,~
## $ Series_Complete_Pfizer_18Plus          <dbl> 27760, 1871626, 549664, 4107032~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 3, 12778, 19, 641, 40177, 3, 52~
## $ Series_Complete_Janssen_65Plus         <dbl> 120, 36069, 11781, 91482, 21518~
## $ Series_Complete_Moderna_65Plus         <dbl> 503, 482492, 109858, 1080575, 1~
## $ Series_Complete_Pfizer_65Plus          <dbl> 2501, 446024, 128135, 1125265, ~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 0, 6528, 7, 306, 22063, 0, 248,~
## $ Additional_Doses                       <dbl> 21262, 1668987, 579418, 3823362~
## $ Additional_Doses_Vax_Pct               <dbl> 49.0, 44.6, 52.1, 43.2, 40.8, 3~
## $ Additional_Doses_12Plus                <dbl> 21249, 1666974, 577890, 3816418~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 54.1, 45.7, 54.5, 44.8, 41.8, 3~
## $ Additional_Doses_18Plus                <dbl> 19639, 1629881, 552365, 3685510~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 57.5, 47.3, 56.3, 46.1, 43.3, 3~
## $ Additional_Doses_50Plus                <dbl> 9565, 1136940, 355796, 2539831,~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 70.3, 59.5, 71.7, 56.7, 55.1, 4~
## $ Additional_Doses_65Plus                <dbl> 2374, 672253, 199142, 1480123, ~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 76.0, 69.2, 79.7, 64.4, 63.1, 5~
## $ Additional_Doses_Moderna               <dbl> 4188, 704060, 257346, 1669451, ~
## $ Additional_Doses_Pfizer                <dbl> 16861, 937786, 315808, 2092068,~
## $ Additional_Doses_Janssen               <dbl> 213, 22577, 6260, 61759, 106718~
## $ Additional_Doses_Unk_Manuf             <dbl> 0, 4564, 4, 84, 8319, 0, 395, 2~
## $ Administered_Dose1_Recip_5Plus         <dbl> 45587, 4264774, 1241337, 109476~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 94.8, 66.4, 93.3, 90.4, 84.2, 5~
## $ Series_Complete_5Plus                  <dbl> 43379, 3742071, 1112304, 884561~
## $ Series_Complete_5PlusPop_Pct           <dbl> 90.2, 58.3, 83.6, 73.1, 71.1, 4~
## $ Administered_5Plus                     <dbl> 109747, 10127951, 2983465, 2349~
## $ Admin_Per_100k_5Plus                   <dbl> 228155, 157742, 224211, 194139,~
## $ Distributed_Per_100k_5Plus             <dbl> 264293, 206644, 269227, 261247,~
## $ Series_Complete_Moderna_5Plus          <dbl> 5225, 1328142, 366985, 3165623,~
## $ Series_Complete_Pfizer_5Plus           <dbl> 36982, 2163886, 679732, 4951801~
## $ Series_Complete_Janssen_5Plus          <dbl> 1169, 237039, 65567, 727500, 13~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 3, 13004, 20, 694, 41363, 3, 53~
## $ Second_Booster                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>     <dbl>        <dbl>     <dbl>
## 1 before  2.66e+10    4.17e+8   8.36e+7 985417       50799    
## 2 after   2.65e+10    4.15e+8   8.29e+7 980521       43911    
## 3 pctchg  5.65e- 3    4.43e-3   9.10e-3      0.00497     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 43,911
## Columns: 6
## $ date       <date> 2021-03-11, 2021-02-12, 2021-08-25, 2022-05-30, 2020-07-23~
## $ state      <chr> "KS", "UT", "CO", "AK", "TX", "CO", "MA", "GA", "TX", "OK",~
## $ tot_cases  <dbl> 297229, 359641, 608176, 251425, 361125, 58307, 704796, 1187~
## $ tot_deaths <dbl> 4851, 1785, 7088, 1252, 7981, 1944, 17818, 21690, 0, 14010,~
## $ new_cases  <dbl> 0, 1060, 1974, 0, 9507, 223, 451, 3829, 0, 0, 2766, 18997, ~
## $ new_deaths <dbl> 0, 11, 4, 0, 281, 0, 5, 7, 0, 0, 3, 417, 7, 0, 0, 9, 15, 31~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.75e+7    4.10e+7 1066764      44129     
## 2 after  4.73e+7    4.08e+7 1046530      42234     
## 3 pctchg 4.99e-3    4.76e-3       0.0190     0.0429
## 
## 
## Processed for cdcHosp:
## Rows: 42,234
## Columns: 5
## $ date       <date> 2020-10-13, 2020-10-09, 2020-10-04, 2020-09-30, 2020-09-22~
## $ state      <chr> "RI", "VT", "UT", "RI", "VT", "VT", "RI", "AK", "RI", "DE",~
## $ inp        <dbl> 124, 0, 179, 91, 1, 4, 78, 50, 74, 89, 42, 69, 33, 8, 44, 6~
## $ hosp_adult <dbl> 123, 0, 160, 90, 1, 4, 78, 49, 74, 84, 42, 66, 33, 7, 42, 5~
## $ hosp_ped   <dbl> 1, 0, 6, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3,~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 3.83e+11 1.58e+11 1406862.    4.05e+10 2098178.    1.47e+11 1662208.   
## 2 after  1.84e+11 7.65e+10 1178390.    1.96e+10 1861606.    7.10e+10 1407029.   
## 3 pctchg 5.18e- 1 5.16e- 1       0.162 5.16e- 1       0.113 5.16e- 1       0.154
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 27,285
## Columns: 9
## $ date        <date> 2022-06-01, 2022-06-01, 2022-06-01, 2022-06-01, 2022-06-0~
## $ state       <chr> "TN", "HI", "PA", "FL", "MT", "NE", "GA", "AK", "UT", "MI"~
## $ vxa         <dbl> 10128911, 2985746, 23503533, 38132367, 1615900, 3251394, 1~
## $ vxc         <dbl> 3742228, 1113175, 8846970, 14462011, 609664, 1234466, 5850~
## $ vxcpoppct   <dbl> 54.8, 78.6, 69.1, 67.3, 57.0, 63.8, 55.1, 62.7, 64.6, 60.5~
## $ vxcgte65    <dbl> 971113, 249781, 2297628, 4119946, 178534, 288998, 1273888,~
## $ vxcgte65pct <dbl> 84.9, 93.0, 95.0, 91.6, 86.5, 92.5, 84.0, 86.6, 94.2, 88.8~
## $ vxcgte18    <dbl> 3448630, 981204, 7997796, 13325670, 556584, 1094822, 53117~
## $ vxcgte18pct <dbl> 64.8, 87.9, 78.7, 77.3, 66.2, 75.1, 65.5, 73.7, 77.9, 69.7~
## 
## Integrated per capita data file:
## Rows: 44,124
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220602, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220602 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220602.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 474,854
## Columns: 128
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         8897
## 2 Critical Access Hospitals 127066
## 3 Long Term                  32599
## 4 Short Term                306292
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       40
## 2 GU      190
## 3 MP       91
## 4 PR     5195
## 5 VI      190
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        42502   431481             871 474854
## 2 all_adult_hospital_inpatient_bed_occupi~  3318   432608           38928 474854
## 3 icu_beds_used_7_day_avg                   1651   415692           57511 474854
## 4 inpatient_beds_7_day_avg                  1732   471229            1893 474854
## 5 staffed_icu_adult_patients_confirmed_an~  4241   330351          140262 474854
## 6 total_adult_patients_hospitalized_confi~  2362   326205          146287 474854
## 7 total_beds_7_day_avg                     36207   438185             462 474854
## 8 total_icu_beds_7_day_avg                  2066   448909           23879 474854
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220602, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220602 <- postProcessCDCDaily(cdc_daily_220602, 
                                              dataThruLabel="May 2022", 
                                              keyDatesBurden=c("2022-05-31", "2021-11-30", 
                                                               "2021-05-31", "2020-11-30"
                                                               ),
                                              keyDatesVaccine=c("2022-05-31", "2022-01-31", 
                                                                "2021-09-30", "2021-05-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220602 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220602, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220602$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n())
## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can override using the `.groups` argument.
## # A tibble: 18 x 6
## # Groups:   adultPed, confSusp, age [18]
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con~  42727 44129
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con~ 255350 44129
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con~ 373410 44129
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con~ 461672 44129
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con~ 735828 44129
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con~ 946224 44129
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con~ 927067 44129
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con~ 801033 44129
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus~  34599 44129
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus~ 233691 44129
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus~ 306167 44129
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus~ 312231 44129
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus~ 492966 44129
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus~ 674311 44129
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus~ 653272 44129
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus~ 594900 44129
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid~ 136757 44129
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid~ 332062 44129

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220602)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 6,576 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 6,566 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity plots are also updated:

modStateHosp_20220602 <- hospitalCapacityCDCDaily(indivHosp_20220602, plotSub="August 2020 to May 2022")

The latest data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220704.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220704.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220704.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220602")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220602")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220602")$dfRaw$vax
                    )

cdc_daily_220704 <- readRunCDCDaily(thruLabel="Jul 2, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## -- Column specification --------------------------------------------------------
## cols(
##   submission_date = col_character(),
##   state = col_character(),
##   tot_cases = col_double(),
##   conf_cases = col_double(),
##   prob_cases = col_double(),
##   new_case = col_double(),
##   pnew_case = col_double(),
##   tot_death = col_double(),
##   conf_death = col_double(),
##   prob_death = col_double(),
##   new_death = col_double(),
##   pnew_death = col_double(),
##   created_at = col_character(),
##   consent_cases = col_character(),
##   consent_deaths = col_character()
## )
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 31
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2020-02-04  tot_cases       19       11        8 0.53333333
## 2  2020-02-05  tot_cases       19       11        8 0.53333333
## 3  2020-02-06  tot_cases       19       11        8 0.53333333
## 4  2020-02-14  tot_cases       24       14       10 0.52631579
## 5  2020-02-15  tot_cases       24       14       10 0.52631579
## 6  2020-02-16  tot_cases       24       14       10 0.52631579
## 7  2020-02-12  tot_cases       22       13        9 0.51428571
## 8  2020-02-07  tot_cases       20       12        8 0.50000000
## 9  2020-02-08  tot_cases       20       12        8 0.50000000
## 10 2020-02-09  tot_cases       20       12        8 0.50000000
## 11 2020-02-10  tot_cases       20       12        8 0.50000000
## 12 2020-02-11  tot_cases       20       12        8 0.50000000
## 13 2020-02-13  tot_cases       23       14        9 0.48648649
## 14 2020-02-17  tot_cases       26       16       10 0.47619048
## 15 2020-02-03  tot_cases       17       11        6 0.42857143
## 16 2020-02-18  tot_cases       31       21       10 0.38461538
## 17 2020-02-19  tot_cases       34       24       10 0.34482759
## 18 2020-02-20  tot_cases       35       25       10 0.33333333
## 19 2020-02-22  tot_cases       48       36       12 0.28571429
## 20 2020-02-23  tot_cases       48       36       12 0.28571429
## 21 2020-02-21  tot_cases       40       30       10 0.28571429
## 22 2020-02-24  tot_cases       52       40       12 0.26086957
## 23 2020-02-25  tot_cases       56       44       12 0.24000000
## 24 2020-02-26  tot_cases       64       52       12 0.20689655
## 25 2020-02-27  tot_cases       69       57       12 0.19047619
## 26 2020-02-28  tot_cases       73       61       12 0.17910448
## 27 2020-02-29  tot_cases       82       70       12 0.15789474
## 28 2020-03-01  tot_cases      100       88       12 0.12765957
## 29 2020-03-02  tot_cases      135      124       11 0.08494208
## 30 2020-03-03  tot_cases      186      175       11 0.06094183
## 31 2022-05-29 new_deaths       52       21       31 0.84931507
## 32 2022-05-30 new_deaths       76       32       44 0.81481481
## 33 2022-05-28 new_deaths       93       48       45 0.63829787
## 34 2022-05-22 new_deaths       70       53       17 0.27642276
## 35 2022-05-21 new_deaths      101       81       20 0.21978022
## 36 2022-04-16 new_deaths       47       40        7 0.16091954
## 37 2022-05-15 new_deaths       63       55        8 0.13559322
## 38 2022-05-31 new_deaths      367      330       37 0.10616930
## 39 2022-04-24 new_deaths       62       56        6 0.10169492
## 40 2022-05-26 new_deaths      322      295       27 0.08752026
## 41 2022-05-23 new_deaths      235      216       19 0.08425721
## 42 2022-04-03 new_deaths      119      110        9 0.07860262
## 43 2022-04-30 new_deaths       97       90        7 0.07486631
## 44 2022-05-28  new_cases    41154    30004    11150 0.31338711
## 45 2022-05-29  new_cases    39447    30180     9267 0.26618984
## 46 2022-05-31  new_cases   158170   183383    25213 0.14763741
## 47 2022-05-30  new_cases    56512    50736     5776 0.10771296

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     DE tot_deaths   1270880   1266093     4787 0.003773789
## 2     NC tot_deaths   9687566   9653360    34206 0.003537163
## 3     KY tot_deaths   5882901   5864807    18094 0.003080431
## 4    FSM  tot_cases      2790      2927      137 0.047927235
## 5     CO  tot_cases 444926166 440093591  4832575 0.010920830
## 6     KY  tot_cases 407328006 406058926  1269080 0.003120483
## 7     NC new_deaths     25091     24660      431 0.017326285
## 8     KY new_deaths     16093     15957      136 0.008486739
## 9     DE new_deaths      2973      2956       17 0.005734525
## 10    FL new_deaths     74905     74557      348 0.004656702
## 11    AL new_deaths     19705     19664       41 0.002082857
## 12   FSM  new_cases        24        26        2 0.080000000
## 13    CO  new_cases   1470642   1437156    33486 0.023031861
## 14    KY  new_cases   1366549   1356440    10109 0.007424929
## 15    SC  new_cases   1507135   1504967     2168 0.001439526
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 53,520
## Columns: 15
## $ date           <date> 2021-12-22, 2021-03-18, 2021-09-01, 2022-03-28, 2021-0~
## $ state          <chr> "DE", "NE", "ND", "VT", "MD", "ID", "IL", "MD", "WI", "~
## $ tot_cases      <dbl> 165076, 206980, 118491, 107785, 390490, 445350, 1130917~
## $ conf_cases     <dbl> 151750, NA, 107475, NA, NA, 348949, 1130917, NA, 22932,~
## $ prob_cases     <dbl> 13326, NA, 11016, NA, NA, 96401, 0, NA, 2548, 0, 225645~
## $ new_cases      <dbl> 662, 298, 536, 467, 924, 0, 2304, 2638, 185, 24, 0, 180~
## $ pnew_case      <dbl> 38, 0, 66, 35, 0, 0, 0, 0, 11, 0, 0, 0, 22, 0, NA, NA, ~
## $ tot_deaths     <dbl> 2345, 2130, 1562, 585, 8549, 4918, 21336, 5410, 700, 2,~
## $ conf_death     <dbl> 2133, NA, NA, NA, 8345, 4007, 19306, 5228, 694, 2, 6958~
## $ prob_death     <dbl> 212, NA, NA, NA, 204, 911, 2030, 182, 6, 0, 3435, NA, 4~
## $ new_deaths     <dbl> 2, 1, 1, 0, 19, 0, 63, 50, 2, 1, 0, 0, 0, 0, 0, 0, 0, 8~
## $ pnew_death     <dbl> 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0~
## $ created_at     <chr> "12/24/2021 12:00:00 AM", "03/20/2021 12:00:00 AM", "09~
## $ consent_cases  <chr> "Agree", "Not agree", "Agree", "Not agree", "N/A", "Agr~
## $ consent_deaths <chr> "Agree", "Not agree", "Not agree", "Not agree", "Agree"~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   state = col_character(),
##   date = col_date(format = ""),
##   geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta   pctDelta
## 1 2022-06-01        inp    29661    27614     2047 0.07147970
## 2 2022-06-01   hosp_ped     1493     1275      218 0.15751445
## 3 2020-08-02   hosp_ped     4087     4737      650 0.14732548
## 4 2020-07-25   hosp_ped     3940     4159      219 0.05408075
## 5 2022-06-01 hosp_adult    28203    26339     1864 0.06835100

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     GA        inp  1971264  1993400    22136 0.011166646
## 2     WA        inp   659079   657786     1293 0.001963755
## 3     NH   hosp_ped     1015      970       45 0.045340050
## 4     ME   hosp_ped     2154     2068       86 0.040738986
## 5     WV   hosp_ped     5026     5143      117 0.023011112
## 6     KS   hosp_ped     4460     4519       59 0.013141775
## 7     GA   hosp_ped    47594    48199      605 0.012631403
## 8     AL   hosp_ped    19364    19177      187 0.009703952
## 9     NM   hosp_ped     7212     7279       67 0.009247119
## 10    NV   hosp_ped     4879     4923       44 0.008977760
## 11    SC   hosp_ped     8399     8473       74 0.008771930
## 12    UT   hosp_ped     9231     9172       59 0.006411998
## 13    MO   hosp_ped    36512    36705      193 0.005272000
## 14    AZ   hosp_ped    25682    25815      133 0.005165349
## 15    VA   hosp_ped    16626    16542       84 0.005065123
## 16    WA   hosp_ped    13265    13202       63 0.004760645
## 17    HI   hosp_ped     2720     2710       10 0.003683241
## 18    SD   hosp_ped     4125     4111       14 0.003399709
## 19    IL   hosp_ped    40566    40695      129 0.003174955
## 20    NJ   hosp_ped    17971    18027       56 0.003111284
## 21    PR   hosp_ped    20069    20125       56 0.002786486
## 22    NE   hosp_ped     7013     7032       19 0.002705589
## 23    PA   hosp_ped    50969    51103      134 0.002625598
## 24    AR   hosp_ped    11525    11555       30 0.002599653
## 25    AK   hosp_ped     2321     2327        6 0.002581756
## 26    WI   hosp_ped    10429    10451       22 0.002107280
## 27    VT   hosp_ped      497      498        1 0.002010050
## 28    MA   hosp_ped    11531    11511       20 0.001735960
## 29    MD   hosp_ped    15246    15272       26 0.001703912
## 30    FL   hosp_ped    87309    87176      133 0.001524486
## 31    OH   hosp_ped    83260    83385      125 0.001500195
## 32    CA   hosp_ped    73628    73725       97 0.001316566
## 33    RI   hosp_ped     3305     3301        4 0.001211020
## 34    GA hosp_adult  1637702  1659233    21531 0.013061222
## 35    WA hosp_adult   585072   583842     1230 0.002104518
## 36    NH hosp_adult    97563    97662       99 0.001014214
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 45,857
## Columns: 135
## $ state                                                                        <chr> ~
## $ date                                                                         <date> ~
## $ critical_staffing_shortage_today_yes                                         <dbl> ~
## $ critical_staffing_shortage_today_no                                          <dbl> ~
## $ critical_staffing_shortage_today_not_reported                                <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> ~
## $ hospital_onset_covid                                                         <dbl> ~
## $ hospital_onset_covid_coverage                                                <dbl> ~
## $ inpatient_beds                                                               <dbl> ~
## $ inpatient_beds_coverage                                                      <dbl> ~
## $ inpatient_beds_used                                                          <dbl> ~
## $ inpatient_beds_used_coverage                                                 <dbl> ~
## $ inp                                                                          <dbl> ~
## $ inpatient_beds_used_covid_coverage                                           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> ~
## $ previous_day_admission_adult_covid_suspected                                 <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> ~
## $ staffed_adult_icu_bed_occupancy                                              <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> ~
## $ hosp_adult                                                                   <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> ~
## $ hosp_ped                                                                     <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> ~
## $ total_staffed_adult_icu_beds                                                 <dbl> ~
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> ~
## $ inpatient_beds_utilization                                                   <dbl> ~
## $ inpatient_beds_utilization_coverage                                          <dbl> ~
## $ inpatient_beds_utilization_numerator                                         <dbl> ~
## $ inpatient_beds_utilization_denominator                                       <dbl> ~
## $ percent_of_inpatients_with_covid                                             <dbl> ~
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> ~
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> ~
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> ~
## $ inpatient_bed_covid_utilization                                              <dbl> ~
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> ~
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> ~
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_covid_utilization                                              <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> ~
## $ adult_icu_bed_utilization                                                    <dbl> ~
## $ adult_icu_bed_utilization_coverage                                           <dbl> ~
## $ adult_icu_bed_utilization_numerator                                          <dbl> ~
## $ adult_icu_bed_utilization_denominator                                        <dbl> ~
## $ geocoded_state                                                               <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> ~
## $ deaths_covid                                                                 <dbl> ~
## $ deaths_covid_coverage                                                        <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> ~
## $ icu_patients_confirmed_influenza                                             <dbl> ~
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> ~
## $ previous_day_admission_influenza_confirmed                                   <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> ~
## $ previous_day_deaths_covid_and_influenza                                      <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> ~
## $ previous_day_deaths_influenza                                                <dbl> ~
## $ previous_day_deaths_influenza_coverage                                       <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> ~
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> ~
## $ all_pediatric_inpatient_beds                                                 <dbl> ~
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> ~
## $ total_staffed_pediatric_icu_beds                                             <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> ~
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   Date = col_character(),
##   Location = col_character()
## )
## i Use `spec()` for the full column specifications.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: Second_Booster_50Plus Second_Booster_50Plus_Vax_Pct Second_Booster_65Plus Second_Booster_65Plus_Vax_Pct Second_Booster_Janssen Second_Booster_Moderna Second_Booster_Pfizer Second_Booster_Unk_Manuf
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 17
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 35,608
## Columns: 91
## $ date                                   <date> 2022-06-29, 2022-06-29, 2022-0~
## $ MMWR_week                              <dbl> 26, 26, 26, 26, 26, 26, 26, 26,~
## $ state                                  <chr> "GU", "NM", "DE", "MI", "CA", "~
## $ Distributed                            <dbl> 330560, 4747945, 2521155, 22699~
## $ Distributed_Janssen                    <dbl> 24100, 188400, 100800, 949600, ~
## $ Distributed_Moderna                    <dbl> 88480, 1843600, 977600, 9043020~
## $ Distributed_Pfizer                     <dbl> 217980, 2715945, 1442755, 12706~
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K                          <dbl> 196191, 226435, 258908, 227291,~
## $ Distributed_Per_100k_5Plus             <dbl> 216390, 240300, 274323, 240958,~
## $ Distributed_Per_100k_12Plus            <dbl> 251729, 265630, 300206, 264098,~
## $ Distributed_Per_100k_18Plus            <dbl> 288588, 292904, 327341, 289423,~
## $ Distributed_Per_100k_65Plus            <dbl> 2058150, 1257380, 1334610, 1285~
## $ vxa                                    <dbl> 365588, 4085449, 1881405, 16368~
## $ Administered_5Plus                     <dbl> 365549, 4084293, 1880661, 16366~
## $ Administered_12Plus                    <dbl> 348267, 3938942, 1824929, 15886~
## $ Administered_18Plus                    <dbl> 313559, 3667648, 1712418, 15013~
## $ Administered_65Plus                    <dbl> 51989, 1113425, 584568, 4842177~
## $ Administered_Janssen                   <dbl> 13414, 119827, 62486, 468873, 2~
## $ Administered_Moderna                   <dbl> 115378, 1658104, 727328, 646501~
## $ Administered_Pfizer                    <dbl> 236410, 2297012, 1089219, 94318~
## $ Administered_Unk_Manuf                 <dbl> 386, 10506, 2372, 2318, 15598, ~
## $ Admin_Per_100k                         <dbl> 216980, 194839, 193210, 163896,~
## $ Admin_Per_100k_5Plus                   <dbl> 239295, 206711, 204632, 173730,~
## $ Admin_Per_100k_12Plus                  <dbl> 265213, 220369, 217303, 184833,~
## $ Admin_Per_100k_18Plus                  <dbl> 273745, 226260, 222337, 191432,~
## $ Admin_Per_100k_65Plus                  <dbl> 323697, 294864, 309449, 274282,~
## $ Recip_Administered                     <dbl> 365895, 4255160, 1858479, 16698~
## $ Administered_Dose1_Recip               <dbl> 154870, 1867238, 820047, 673991~
## $ Administered_Dose1_Pop_Pct             <dbl> 91.9, 89.1, 84.2, 67.5, 83.0, 4~
## $ Administered_Dose1_Recip_5Plus         <dbl> 154834, 1866115, 819427, 673793~
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 95.0, 94.4, 89.2, 71.5, 88.1, 5~
## $ Administered_Dose1_Recip_12Plus        <dbl> 145443, 1781472, 789949, 649181~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 95.0, 94.1, 75.5, 92.7, 5~
## $ Administered_Dose1_Recip_18Plus        <dbl> 129366, 1642637, 739120, 609558~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 77.7, 93.7, 5~
## $ Administered_Dose1_Recip_65Plus        <dbl> 17745, 437406, 215028, 1716056,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 4~
## $ vxc                                    <dbl> 140333, 1509192, 682756, 605968~
## $ vxcpoppct                              <dbl> 83.3, 72.0, 70.1, 60.7, 72.8, 3~
## $ Series_Complete_5Plus                  <dbl> 140329, 1509107, 682724, 605960~
## $ Series_Complete_5PlusPop_Pct           <dbl> 91.9, 76.4, 74.3, 64.3, 77.4, 4~
## $ Series_Complete_12Plus                 <dbl> 133100, 1444954, 659010, 584134~
## $ Series_Complete_12PlusPop_Pct          <dbl> 95.0, 80.8, 78.5, 68.0, 81.6, 4~
## $ vxcgte18                               <dbl> 118785, 1330134, 615581, 548244~
## $ vxcgte18pct                            <dbl> 95.0, 82.1, 79.9, 69.9, 82.4, 5~
## $ vxcgte65                               <dbl> 16715, 363032, 184932, 1572725,~
## $ vxcgte65pct                            <dbl> 95.0, 95.0, 95.0, 89.1, 91.6, 3~
## $ Series_Complete_Janssen                <dbl> 11228, 110899, 57466, 423472, 2~
## $ Series_Complete_Moderna                <dbl> 40195, 569094, 239174, 2197962,~
## $ Series_Complete_Pfizer                 <dbl> 88745, 826711, 385312, 3437011,~
## $ Series_Complete_Unk_Manuf              <dbl> 165, 2488, 804, 1236, 5063, 3, ~
## $ Series_Complete_Janssen_5Plus          <dbl> 11227, 110889, 57462, 423458, 2~
## $ Series_Complete_Moderna_5Plus          <dbl> 40193, 569057, 239163, 2197936,~
## $ Series_Complete_Pfizer_5Plus           <dbl> 88744, 826674, 385295, 3436979,~
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 165, 2487, 804, 1235, 5061, 3, ~
## $ Series_Complete_Janssen_12Plus         <dbl> 11226, 110880, 57455, 423439, 2~
## $ Series_Complete_Moderna_12Plus         <dbl> 40193, 568999, 239160, 2197875,~
## $ Series_Complete_Pfizer_12Plus          <dbl> 81517, 762602, 361596, 3218815,~
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 164, 2473, 799, 1215, 4980, 3, ~
## $ Series_Complete_Janssen_18Plus         <dbl> 11217, 110737, 57405, 423120, 2~
## $ Series_Complete_Moderna_18Plus         <dbl> 40173, 568568, 239017, 2197100,~
## $ Series_Complete_Pfizer_18Plus          <dbl> 67231, 648399, 318385, 2861129,~
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 164, 2430, 774, 1099, 4662, 3, ~
## $ Series_Complete_Janssen_65Plus         <dbl> 642, 21439, 10024, 71373, 20281~
## $ Series_Complete_Moderna_65Plus         <dbl> 6825, 170464, 76713, 788262, 27~
## $ Series_Complete_Pfizer_65Plus          <dbl> 9205, 169969, 97812, 712464, 24~
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 43, 1160, 383, 626, 1490, 0, 36~
## $ Additional_Doses                       <dbl> 69361, 776169, 323480, 3353818,~
## $ Additional_Doses_Vax_Pct               <dbl> 49.4, 51.4, 47.4, 55.3, 54.5, 3~
## $ Additional_Doses_12Plus                <dbl> 69087, 770329, 322088, 3341400,~
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 51.9, 53.3, 48.9, 57.2, 56.6, 3~
## $ Additional_Doses_18Plus                <dbl> 64829, 734378, 309800, 3231323,~
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 54.6, 55.2, 50.3, 58.9, 58.7, 4~
## $ Additional_Doses_50Plus                <dbl> 33243, 459183, 219856, 2151063,~
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 71.4, 64.7, 61.8, 69.2, 69.8, 5~
## $ Additional_Doses_65Plus                <dbl> 13181, 254948, 130474, 1217144,~
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 78.9, 70.2, 70.6, 77.4, 77.3, 5~
## $ Additional_Doses_Moderna               <dbl> 25618, 323485, 138013, 1482974,~
## $ Additional_Doses_Pfizer                <dbl> 42023, 441374, 180140, 1822184,~
## $ Additional_Doses_Janssen               <dbl> 1714, 10969, 5252, 48489, 23425~
## $ Additional_Doses_Unk_Manuf             <dbl> 6, 341, 75, 171, 703, 0, 175, 1~
## $ Second_Booster                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA,~
## $ Second_Booster_50Plus                  <dbl> 8765, 156782, 64930, 599983, 26~
## $ Second_Booster_50Plus_Vax_Pct          <dbl> 26.4, 34.1, 29.5, 27.9, 31.9, 1~
## $ Second_Booster_65Plus                  <dbl> 4701, 107117, 47944, 426384, 16~
## $ Second_Booster_65Plus_Vax_Pct          <dbl> 35.7, 42.0, 36.7, 35.0, 39.6, 1~
## $ Second_Booster_Janssen                 <dbl> 5, 235, 67, 404, 2029, 0, 195, ~
## $ Second_Booster_Moderna                 <dbl> 3885, 76133, 31765, 296665, 141~
## $ Second_Booster_Pfizer                  <dbl> 5297, 88094, 35113, 325197, 136~
## $ Second_Booster_Unk_Manuf               <dbl> 0, 136, 11, 27, 72, 0, 32, 36, ~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
##   isType tot_cases tot_deaths     new_cases   new_deaths         n
##   <chr>      <dbl>      <dbl>         <dbl>        <dbl>     <dbl>
## 1 before  2.93e+10    4.48e+8 87042181      996716       52628    
## 2 after   2.91e+10    4.46e+8 86169240      991589       45492    
## 3 pctchg  6.01e- 3    4.47e-3        0.0100      0.00514     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 45,492
## Columns: 6
## $ date       <date> 2021-12-22, 2021-03-18, 2021-09-01, 2022-03-28, 2021-03-11~
## $ state      <chr> "DE", "NE", "ND", "VT", "MD", "ID", "IL", "MD", "WI", "CA",~
## $ tot_cases  <dbl> 165076, 206980, 118491, 107785, 390490, 445350, 1130917, 23~
## $ tot_deaths <dbl> 2345, 2130, 1562, 585, 8549, 4918, 21336, 5410, 700, 2, 103~
## $ new_cases  <dbl> 662, 298, 536, 467, 924, 0, 2304, 2638, 185, 24, 0, 180, 55~
## $ new_deaths <dbl> 2, 1, 1, 0, 19, 0, 63, 50, 2, 1, 0, 0, 0, 0, 0, 0, 8, 53, 0~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.85e+7    4.20e+7 1112611      45857     
## 2 after  4.83e+7    4.18e+7 1090781      43866     
## 3 pctchg 5.16e-3    4.93e-3       0.0196     0.0434
## 
## 
## Processed for cdcHosp:
## Rows: 43,866
## Columns: 5
## $ date       <date> 2020-12-19, 2020-10-04, 2020-10-02, 2020-09-25, 2020-09-23~
## $ state      <chr> "SD", "WY", "VT", "AK", "AK", "VT", "VT", "VT", "VT", "SD",~
## $ inp        <dbl> 335, 58, 0, 52, 51, 1, 4, 5, 3, 109, 2, 37, 9, 81, 80, 42, ~
## $ hosp_adult <dbl> 332, 57, 0, 51, 48, 1, 4, 5, 3, 74, 2, 36, 6, 76, 79, 39, 5~
## $ hosp_ped   <dbl> 3, 1, 0, 1, 2, 0, 0, 0, 0, 29, 0, 1, 1, 0, 1, 2, 0, 2, 0, 0~
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>       <dbl>    <dbl>       <dbl>
## 1 before 4.03e+11 1.66e+11 1475157.    4.22e+10 2189089.    1.54e+11 1739833    
## 2 after  1.94e+11 8.02e+10 1235107.    2.04e+10 1940160.    7.43e+10 1472279.   
## 3 pctchg 5.18e- 1 5.16e- 1       0.163 5.16e- 1       0.114 5.16e- 1       0.154
## # ... with 1 more variable: n <dbl>
## 
## 
## Processed for vax:
## Rows: 28,152
## Columns: 9
## $ date        <date> 2022-06-29, 2022-06-29, 2022-06-29, 2022-06-29, 2022-06-2~
## $ state       <chr> "NM", "DE", "MI", "CA", "WI", "DC", "NY", "TN", "AK", "AL"~
## $ vxa         <dbl> 4085449, 1881405, 16368051, 77764209, 10648880, 1594848, 4~
## $ vxc         <dbl> 1509192, 682756, 6059681, 28773716, 3849301, 537548, 15135~
## $ vxcpoppct   <dbl> 72.0, 70.1, 60.7, 72.8, 66.1, 76.2, 77.8, 54.9, 62.7, 51.6~
## $ vxcgte65    <dbl> 363032, 184932, 1572725, 5348260, 983408, 86544, 3082249, ~
## $ vxcgte65pct <dbl> 95.0, 95.0, 89.1, 91.6, 95.0, 95.0, 93.5, 85.2, 86.8, 82.9~
## $ vxcgte18    <dbl> 1330134, 615581, 5482448, 25229699, 3453855, 490080, 13543~
## $ vxcgte18pct <dbl> 82.1, 79.9, 69.9, 82.4, 75.8, 84.9, 87.8, 64.9, 73.5, 61.6~
## 
## Integrated per capita data file:
## Rows: 45,756
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition

saveToRDS(cdc_daily_220704, ovrWriteError=FALSE)

# Run for latest data, save as RDS
indivHosp_20220704 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220704.csv")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   hospital_pk = col_character(),
##   collection_week = col_date(format = ""),
##   state = col_character(),
##   ccn = col_character(),
##   hospital_name = col_character(),
##   address = col_character(),
##   city = col_character(),
##   zip = col_character(),
##   hospital_subtype = col_character(),
##   fips_code = col_character(),
##   is_metro_micro = col_logical(),
##   geocoded_hospital_address = col_character(),
##   hhs_ids = col_character(),
##   is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.
## Rows: 494,918
## Columns: 128
## $ hospital_pk                                                                        <chr> ~
## $ collection_week                                                                    <date> ~
## $ state                                                                              <chr> ~
## $ ccn                                                                                <chr> ~
## $ hospital_name                                                                      <chr> ~
## $ address                                                                            <chr> ~
## $ city                                                                               <chr> ~
## $ zip                                                                                <chr> ~
## $ hospital_subtype                                                                   <chr> ~
## $ fips_code                                                                          <chr> ~
## $ is_metro_micro                                                                     <lgl> ~
## $ total_beds_7_day_avg                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> ~
## $ inpatient_beds_used_7_day_avg                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> ~
## $ inpatient_beds_7_day_avg                                                           <dbl> ~
## $ total_icu_beds_7_day_avg                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> ~
## $ icu_beds_used_7_day_avg                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> ~
## $ total_beds_7_day_sum                                                               <dbl> ~
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> ~
## $ inpatient_beds_used_7_day_sum                                                      <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> ~
## $ inpatient_beds_7_day_sum                                                           <dbl> ~
## $ total_icu_beds_7_day_sum                                                           <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> ~
## $ icu_beds_used_7_day_sum                                                            <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> ~
## $ total_beds_7_day_coverage                                                          <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> ~
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> ~
## $ inpatient_beds_7_day_coverage                                                      <dbl> ~
## $ total_icu_beds_7_day_coverage                                                      <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> ~
## $ icu_beds_used_7_day_coverage                                                       <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> ~
## $ geocoded_hospital_address                                                          <chr> ~
## $ hhs_ids                                                                            <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> ~
## $ is_corrected                                                                       <lgl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> ~
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> ~
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> ~
## 
## Hospital Subtype Counts:
## # A tibble: 4 x 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         9372
## 2 Critical Access Hospitals 132455
## 3 Long Term                  33970
## 4 Short Term                319121
## 
## Records other than 50 states and DC
## # A tibble: 5 x 2
##   state     n
##   <chr> <int>
## 1 AS       44
## 2 GU      198
## 3 MP       95
## 4 PR     5407
## 5 VI      198
## 
## Record types for key metrics
## # A tibble: 8 x 5
##   name                                      `NA` Positive `Value -999999`  Total
##   <chr>                                    <int>    <int>           <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg        50874   443132             912 494918
## 2 all_adult_hospital_inpatient_bed_occupi~  3319   450806           40793 494918
## 3 icu_beds_used_7_day_avg                   1653   433066           60199 494918
## 4 inpatient_beds_7_day_avg                  1733   491216            1969 494918
## 5 staffed_icu_adult_patients_confirmed_an~  4245   342951          147722 494918
## 6 total_adult_patients_hospitalized_confi~  2363   338877          153678 494918
## 7 total_beds_7_day_avg                     44507   449926             485 494918
## 8 total_icu_beds_7_day_avg                  2067   467915           24936 494918
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220704, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220704 <- postProcessCDCDaily(cdc_daily_220704, 
                                              dataThruLabel="Jun 2022", 
                                              keyDatesBurden=c("2022-06-30", "2021-12-31", 
                                                               "2021-06-30", "2020-12-31"
                                                               ),
                                              keyDatesVaccine=c("2022-06-29", "2022-02-28", 
                                                                "2021-10-31", "2021-06-30"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220704 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220704, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220704$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 x 6
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con~  43797 45857
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con~ 263705 45857
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con~ 384671 45857
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con~ 470926 45857
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con~ 751006 45857
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con~ 970735 45857
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con~ 958201 45857
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con~ 838101 45857
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus~  36051 45857
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus~ 241076 45857
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus~ 315976 45857
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus~ 321335 45857
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus~ 507664 45857
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus~ 695927 45857
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus~ 675237 45857
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus~ 614420 45857
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid~ 145408 45857
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid~ 344599 45857

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220704)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 6,960 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 6,950 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity plots are also updated:

modStateHosp_20220704 <- hospitalCapacityCDCDaily(indivHosp_20220704, plotSub="August 2020 to June 2022")

The latest data are downloaded and processed:

# Update to helperSummaryMap for updated usmap::usmap_transform
helperSummaryMap <- function(df, 
                             mapLevel="states", 
                             keyCol="state",
                             values="cluster",
                             discreteValues=NULL,
                             legend.position="right",
                             labelScale=TRUE,
                             extraArgs=list(),
                             countOnly=FALSE,
                             textLabel=c(),
                             ...
                             ) {
    
    # FUNCTION ARGUMENTS:
    # df: a data frame containing a level of geography and an associated cluster
    # mapLevel: a parameter for whether the map is "states" or "counties"
    # keyCol: the key column for plotting (usmap::plot_usmap is particular, and this must be 'state' or 'fips')
    # values: the character name of the field containing the data to be plotted
    # discreteValues: boolean for whether the values are discrete (if not, use continuous)
    #                 NULL means infer from data
    # legend.position: character for the location of the legend in the plot
    # labelScale: boolean, should an scale_fill_ be created?  Use FALSE if contained in extraArgs
    # extraArgs: list of other arguments that will be appended as '+' to the end of the usmap::plot_usmap call
    # countOnly: should a bar plot of counts only be produced?
    # textLabel: a list of elements that should be labelled as text on the plot (too small to see)
    # ...: other parameters to be passed to usmap::plot_usmap (e.g., labels, include, exclude, etc.)
    
    # Modify the data frame to contain only the relevant data
    df <- df %>%
        select(all_of(c(keyCol, values))) %>%
        distinct()
    
    # Determine the type of data being plotted
    if (is.null(discreteValues)) discreteValues <- !is.numeric(df[[values]])
    
    # Convert data type if needed
    if (isTRUE(discreteValues) & is.numeric(df[[values]])) 
        df[[values]] <- factor(df[[values]])
    
    # If count only is needed, create a count map; otherwise create a map
    if (isTRUE(countOnly)) { 
        gg <- df %>%
            ggplot(aes(x=fct_rev(get(values)))) + 
            geom_bar(aes_string(fill=values)) + 
            stat_count(aes(label=..count.., y=..count../2), 
                       geom="text", 
                       position="identity", 
                       fontface="bold"
                       ) +
            coord_flip() + 
            labs(y="Number of members", x="")
    } else {
        gg <- usmap::plot_usmap(regions=mapLevel, data=df, values=values, ...)
        if (length(textLabel) > 0) {
            labDF <- df %>% 
                filter(get(keyCol) %in% textLabel) %>%
                mutate(rk=match(get(keyCol), textLabel)) %>%
                arrange(rk) %>%
                mutate(lon=-70.1-seq(0, 0.8*length(textLabel)-0.8, by=0.8), 
                       lat=40.1-seq(0, 1.5*length(textLabel)-1.5, by=1.5)
                       ) %>%
                select(lon, lat, everything()) %>%
                usmap::usmap_transform(output_names=c("lon.1", "lat.1"))
            gg <- gg + geom_text(data=labDF, 
                                 aes(x=lon.1, y=lat.1, label=paste(get(keyCol), get(values))), 
                                 size=3.25
                                 )
        }
    }
    
    # Position the legend as requested
    gg <- gg + theme(legend.position=legend.position)
    
    # Create the scale if appropriate
    if (isTRUE(labelScale)) gg <- gg + 
        if(isTRUE(discreteValues)) scale_fill_discrete(values) else scale_fill_continuous(values)
    
    # Apply extra arguments
    for (ctr in seq_along(extraArgs)) gg <- gg + extraArgs[[ctr]]
    
    # Return the map object
    gg
    
}

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220805.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220805.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220805.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220704")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220704")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220704")$dfRaw$vax
                    )

cdc_daily_220805 <- readRunCDCDaily(thruLabel="Aug 3, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## 
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_220805.csv
## Rows: 55500 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-06-26 new_deaths       72       29       43 0.85148515
## 2  2022-06-25 new_deaths      109       62       47 0.54970760
## 3  2022-06-19 new_deaths      114       89       25 0.24630542
## 4  2022-06-18 new_deaths       97       82       15 0.16759777
## 5  2022-06-27 new_deaths      291      247       44 0.16356877
## 6  2022-06-30 new_deaths      486      415       71 0.15760266
## 7  2022-06-20 new_deaths      145      127       18 0.13235294
## 8  2022-06-28 new_deaths      608      537       71 0.12401747
## 9  2022-05-29 new_deaths       58       52        6 0.10909091
## 10 2022-06-29 new_deaths      583      524       59 0.10659440
## 11 2022-06-24 new_deaths      454      410       44 0.10185185
## 12 2022-07-01 new_deaths      538      487       51 0.09951220
## 13 2022-06-12 new_deaths       95       86        9 0.09944751
## 14 2022-05-30 new_deaths       83       76        7 0.08805031
## 15 2022-06-23 new_deaths      499      459       40 0.08350731
## 16 2022-05-14 new_deaths       82       76        6 0.07594937
## 17 2022-06-16 new_deaths      304      286       18 0.06101695
## 18 2022-06-05 new_deaths      124      117        7 0.05809129
## 19 2022-06-13 new_deaths      296      280       16 0.05555556
## 20 2022-06-22 new_deaths      626      593       33 0.05414274
## 21 2022-07-01  new_cases   172854   158017    14837 0.08968450
## 22 2022-06-30  new_cases   118066   110590     7476 0.06539081

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     KY tot_deaths   6398010   6382749    15261 0.002388121
## 2     NC tot_deaths  10480735  10467772    12963 0.001237606
## 3     CO  tot_cases 493149559 491619869  1529690 0.003106697
## 4     KY new_deaths     16365     16182      183 0.011245276
## 5     NC new_deaths     25435     25211      224 0.008845713
## 6     FL new_deaths     76444     75891      553 0.007260314
## 7     AL new_deaths     19823     19776       47 0.002373797
## 8     KY  new_cases   1416476   1406705     9771 0.006921979
## 9     CO  new_cases   1547593   1537672     9921 0.006431214
## 10    SC  new_cases   1555208   1546406     8802 0.005675755
## 11    NC  new_cases   2883706   2869560    14146 0.004917555
## 12    CT  new_cases    817234    819923     2689 0.003284963
## 13    FL  new_cases   6504443   6493975    10468 0.001610658
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 55,500
## Columns: 15
## $ date           <date> 2022-01-14, 2020-07-11, 2022-01-02, 2020-02-04, 2022-0…
## $ state          <chr> "KS", "TN", "AS", "AR", "AK", "PA", "TX", "PW", "AS", "…
## $ tot_cases      <dbl> 621273, 59582, 11, 0, 251425, 86552, 361125, 0, 0, 1226…
## $ conf_cases     <dbl> 470516, 59137, NA, NA, NA, 84260, NA, NA, NA, NA, NA, N…
## $ prob_cases     <dbl> 150757, 445, NA, NA, NA, 2292, NA, NA, NA, NA, NA, NA, …
## $ new_cases      <dbl> 19414, 1964, 0, 0, 0, 459, 9507, 0, 0, 28, 2, 8, 2293, …
## $ pnew_case      <dbl> 6964, 28, 0, NA, 0, 18, 0, 0, 0, 5, 0, 0, 552, 46, 70, …
## $ tot_deaths     <dbl> 7162, 723, 0, 0, 1252, 6426, 7981, 0, 0, 1967, 0, 17, 1…
## $ conf_death     <dbl> NA, 697, NA, NA, NA, NA, NA, NA, NA, 1601, 0, NA, 18646…
## $ prob_death     <dbl> NA, 26, NA, NA, NA, NA, NA, NA, NA, 366, 0, NA, 0, 360,…
## $ new_deaths     <dbl> 21, 13, 0, 0, 0, 0, 281, 0, 0, 0, 0, 0, 0, 5, 0, 3, 0, …
## $ pnew_death     <dbl> 4, 0, 0, NA, 0, -264, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at     <chr> "01/15/2022 02:59:30 PM", "07/10/2020 12:00:00 AM", "01…
## $ consent_cases  <chr> "Agree", "Agree", NA, "Not agree", "N/A", "Agree", "Not…
## $ consent_deaths <chr> "N/A", "Agree", NA, "Not agree", "N/A", "Not agree", "N…
## 
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_220805.csv
## Rows: 47585 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr    (1): state
## dbl  (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl    (1): geocoded_state
## date   (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta  pctDelta
## 1 2020-07-25 hosp_ped     4594     3940      654 0.1532693
## 2 2020-08-02 hosp_ped     4712     4087      625 0.1420616

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     AS        inp      543      541        2 0.003690037
## 2     NH   hosp_ped     1122     1073       49 0.044646925
## 3     WV   hosp_ped     5454     5257      197 0.036784614
## 4     KS   hosp_ped     4498     4589       91 0.020028612
## 5     ID   hosp_ped     3835     3765       70 0.018421053
## 6     NV   hosp_ped     5124     5190       66 0.012798138
## 7     VA   hosp_ped    17305    17454      149 0.008573319
## 8     AR   hosp_ped    11989    11892       97 0.008123613
## 9     KY   hosp_ped    19577    19435      142 0.007279811
## 10    NM   hosp_ped     7703     7648       55 0.007165657
## 11    TN   hosp_ped    21368    21220      148 0.006950315
## 12    MO   hosp_ped    37949    37698      251 0.006636086
## 13    MA   hosp_ped    12147    12225       78 0.006400788
## 14    NJ   hosp_ped    18742    18632      110 0.005886445
## 15    UT   hosp_ped     9659     9715       56 0.005780944
## 16    IN   hosp_ped    17135    17042       93 0.005442256
## 17    ME   hosp_ped     2259     2249       10 0.004436557
## 18    PR   hosp_ped    21623    21710       87 0.004015416
## 19    MS   hosp_ped    11012    10974       38 0.003456745
## 20    SC   hosp_ped     8605     8634       29 0.003364464
## 21    IL   hosp_ped    42493    42377      116 0.002733593
## 22    CO   hosp_ped    21168    21111       57 0.002696374
## 23    PA   hosp_ped    53172    53065      107 0.002014364
## 24    NC   hosp_ped    29449    29505       56 0.001899786
## 25    FL   hosp_ped    89852    90020      168 0.001867995
## 26    AZ   hosp_ped    26450    26403       47 0.001778518
## 27    RI   hosp_ped     3473     3479        6 0.001726122
## 28    OR   hosp_ped    10977    10993       16 0.001456532
## 29    MD   hosp_ped    16245    16224       21 0.001293542
## 30    HI   hosp_ped     3171     3167        4 0.001262228
## 31    WY   hosp_ped      823      822        1 0.001215805
## 32    SD   hosp_ped     4194     4199        5 0.001191469
## 33    DE   hosp_ped     5065     5059        6 0.001185302
## 34    NY   hosp_ped    71294    71213       81 0.001136786
## 35    TX   hosp_ped   114450   114322      128 0.001119018
## 36    CA   hosp_ped    78112    78026       86 0.001101590
## 37    ND   hosp_ped     3651     3647        4 0.001096191
## 38    AS hosp_adult      529      527        2 0.003787879
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 47,585
## Columns: 135
## $ state                                                                        <chr> …
## $ date                                                                         <date> …
## $ critical_staffing_shortage_today_yes                                         <dbl> …
## $ critical_staffing_shortage_today_no                                          <dbl> …
## $ critical_staffing_shortage_today_not_reported                                <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> …
## $ hospital_onset_covid                                                         <dbl> …
## $ hospital_onset_covid_coverage                                                <dbl> …
## $ inpatient_beds                                                               <dbl> …
## $ inpatient_beds_coverage                                                      <dbl> …
## $ inpatient_beds_used                                                          <dbl> …
## $ inpatient_beds_used_coverage                                                 <dbl> …
## $ inp                                                                          <dbl> …
## $ inpatient_beds_used_covid_coverage                                           <dbl> …
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> …
## $ previous_day_admission_adult_covid_suspected                                 <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> …
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> …
## $ staffed_adult_icu_bed_occupancy                                              <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> …
## $ hosp_adult                                                                   <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> …
## $ hosp_ped                                                                     <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> …
## $ total_staffed_adult_icu_beds                                                 <dbl> …
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> …
## $ inpatient_beds_utilization                                                   <dbl> …
## $ inpatient_beds_utilization_coverage                                          <dbl> …
## $ inpatient_beds_utilization_numerator                                         <dbl> …
## $ inpatient_beds_utilization_denominator                                       <dbl> …
## $ percent_of_inpatients_with_covid                                             <dbl> …
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> …
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> …
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> …
## $ inpatient_bed_covid_utilization                                              <dbl> …
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> …
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> …
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_covid_utilization                                              <dbl> …
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> …
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> …
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_utilization                                                    <dbl> …
## $ adult_icu_bed_utilization_coverage                                           <dbl> …
## $ adult_icu_bed_utilization_numerator                                          <dbl> …
## $ adult_icu_bed_utilization_denominator                                        <dbl> …
## $ geocoded_state                                                               <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> …
## $ deaths_covid                                                                 <dbl> …
## $ deaths_covid_coverage                                                        <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> …
## $ icu_patients_confirmed_influenza                                             <dbl> …
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> …
## $ previous_day_admission_influenza_confirmed                                   <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> …
## $ previous_day_deaths_covid_and_influenza                                      <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> …
## $ previous_day_deaths_influenza                                                <dbl> …
## $ previous_day_deaths_influenza_coverage                                       <dbl> …
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> …
## $ all_pediatric_inpatient_beds                                                 <dbl> …
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> …
## $ total_staffed_pediatric_icu_beds                                             <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> …
## 
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_220805.csv
## Rows: 35928 Columns: 93
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Date, Location
## dbl (91): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: Additional_Doses_5Plus Additional_Doses_5Plus_Vax_Pct
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 5
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 35,928
## Columns: 93
## $ date                                   <date> 2022-08-03, 2022-08-03, 2022-0…
## $ MMWR_week                              <dbl> 31, 31, 31, 31, 31, 31, 31, 31,…
## $ state                                  <chr> "AK", "LA", "PW", "FL", "GU", "…
## $ Distributed                            <dbl> 1721465, 8927950, 46890, 520299…
## $ Distributed_Janssen                    <dbl> 85800, 327400, 3800, 2425100, 2…
## $ Distributed_Moderna                    <dbl> 667420, 3668180, 30000, 1935532…
## $ Distributed_Pfizer                     <dbl> 967445, 4927870, 13090, 3023546…
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K                          <dbl> 235319, 192049, 217769, 242251,…
## $ Distributed_Per_100k_5Plus             <dbl> 252984, 205367, 230158, 255827,…
## $ Distributed_Per_100k_12Plus            <dbl> 282728, 227561, 251488, 278294,…
## $ Distributed_Per_100k_18Plus            <dbl> 312107, 250703, 282759, 301661,…
## $ Distributed_Per_100k_65Plus            <dbl> 1879580, 1204820, 2353920, 1156…
## $ vxa                                    <dbl> 1193878, 6490846, 49270, 390355…
## $ Administered_5Plus                     <dbl> 1190378, 6485568, 49256, 390025…
## $ Administered_12Plus                    <dbl> 1143663, 6351067, 46584, 381763…
## $ Administered_18Plus                    <dbl> 1064124, 6011787, 42928, 362036…
## $ Administered_65Plus                    <dbl> 244166, 1880520, 5330, 13019177…
## $ Administered_Janssen                   <dbl> 45975, 201008, 2356, 1491938, 1…
## $ Administered_Moderna                   <dbl> 460982, 2648043, 37718, 1447846…
## $ Administered_Pfizer                    <dbl> 685618, 3638334, 9031, 22909920…
## $ Administered_Unk_Manuf                 <dbl> 1301, 3353, 165, 154533, 394, 3…
## $ Admin_Per_100k                         <dbl> 163200, 139624, 228822, 181749,…
## $ Admin_Per_100k_5Plus                   <dbl> 174936, 149185, 241771, 191772,…
## $ Admin_Per_100k_12Plus                  <dbl> 187832, 161880, 249847, 204195,…
## $ Admin_Per_100k_18Plus                  <dbl> 192929, 168815, 258868, 209903,…
## $ Admin_Per_100k_65Plus                  <dbl> 266592, 253774, 267570, 289486,…
## $ Recip_Administered                     <dbl> 1208382, 6468369, 49645, 387760…
## $ Administered_Dose1_Recip               <dbl> 520599, 2874375, 20539, 1731201…
## $ Administered_Dose1_Pop_Pct             <dbl> 71.2, 61.8, 95.0, 80.6, 92.4, 0…
## $ Administered_Dose1_Recip_5Plus         <dbl> 518121, 2870474, 20526, 1728636…
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 76.1, 66.0, 95.0, 85.0, 95.0, 0…
## $ Administered_Dose1_Recip_12Plus        <dbl> 494499, 2793348, 19099, 1684615…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 81.2, 71.2, 95.0, 90.1, 95.0, 0…
## $ Administered_Dose1_Recip_18Plus        <dbl> 457624, 2618633, 17567, 1586212…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 83.0, 73.5, 95.0, 92.0, 95.0, 0…
## $ Administered_Dose1_Recip_65Plus        <dbl> 88238, 681022, 1874, 4841075, 1…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.9, 94.1, 95.0, 95.0, 0…
## $ vxc                                    <dbl> 461227, 2517249, 18308, 1459286…
## $ vxcpoppct                              <dbl> 63.0, 54.1, 85.0, 67.9, 83.6, 0…
## $ Series_Complete_5Plus                  <dbl> 460519, 2516780, 18307, 1458923…
## $ Series_Complete_5PlusPop_Pct           <dbl> 67.7, 57.9, 89.9, 71.7, 92.2, 0…
## $ Series_Complete_12Plus                 <dbl> 440334, 2461210, 17225, 1424111…
## $ Series_Complete_12PlusPop_Pct          <dbl> 72.3, 62.7, 92.4, 76.2, 95.0, 0…
## $ vxcgte18                               <dbl> 406880, 2316905, 15778, 1342286…
## $ vxcgte18pct                            <dbl> 73.8, 65.1, 95.0, 77.8, 95.0, 0…
## $ vxcgte65                               <dbl> 79796, 641441, 1809, 4150448, 1…
## $ vxcgte65pct                            <dbl> 87.1, 86.6, 90.8, 92.3, 95.0, 0…
## $ Series_Complete_Janssen                <dbl> 42125, 182512, 2360, 1374210, 1…
## $ Series_Complete_Moderna                <dbl> 162208, 969622, 12715, 4930391,…
## $ Series_Complete_Pfizer                 <dbl> 256605, 1363742, 3152, 8243949,…
## $ Series_Complete_Unk_Manuf              <dbl> 282, 1295, 81, 44095, 169, 0, 2…
## $ Series_Complete_Janssen_5Plus          <dbl> 42123, 182490, 2360, 1373388, 1…
## $ Series_Complete_Moderna_5Plus          <dbl> 161887, 969370, 12715, 4927990,…
## $ Series_Complete_Pfizer_5Plus           <dbl> 256225, 1363606, 3151, 8243747,…
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 282, 1292, 81, 43923, 169, 0, 2…
## $ Series_Complete_Janssen_12Plus         <dbl> 42121, 182478, 2360, 1373333, 1…
## $ Series_Complete_Moderna_12Plus         <dbl> 161820, 969336, 12715, 4927746,…
## $ Series_Complete_Pfizer_12Plus          <dbl> 236115, 1308104, 2069, 7896721,…
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 277, 1270, 81, 43131, 168, 0, 1…
## $ Series_Complete_Janssen_18Plus         <dbl> 41956, 182279, 2360, 1372347, 1…
## $ Series_Complete_Moderna_18Plus         <dbl> 161382, 968751, 12715, 4926033,…
## $ Series_Complete_Pfizer_18Plus          <dbl> 203276, 1164614, 622, 7082202, …
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 265, 1244, 81, 42108, 168, 0, 1…
## $ Series_Complete_Janssen_65Plus         <dbl> 3720, 22498, 227, 215678, 645, …
## $ Series_Complete_Moderna_65Plus         <dbl> 43746, 305326, 1541, 1989561, 6…
## $ Series_Complete_Pfizer_65Plus          <dbl> 32272, 313396, 39, 1922722, 921…
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 58, 219, 2, 22443, 45, 0, 325, …
## $ Additional_Doses                       <dbl> 215249, 1042687, 12000, 6159483…
## $ Additional_Doses_Vax_Pct               <dbl> 46.7, 41.4, 65.5, 42.2, 50.5, 3…
## $ Additional_Doses_5Plus                 <dbl> 215238, 1042609, 12000, 6159391…
## $ Additional_Doses_5Plus_Vax_Pct         <dbl> 46.7, 41.4, 65.5, 42.2, 50.5, 3…
## $ Additional_Doses_12Plus                <dbl> 212925, 1039965, 11834, 6134941…
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 48.4, 42.3, 68.7, 43.1, 53.0, 3…
## $ Additional_Doses_18Plus                <dbl> 202938, 1017274, 11146, 5979633…
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 49.9, 43.9, 70.6, 44.5, 55.6, 3…
## $ Additional_Doses_50Plus                <dbl> 119524, 731352, 4804, 4352080, …
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 65.6, 57.2, 80.1, 56.3, 72.4, 5…
## $ Additional_Doses_65Plus                <dbl> 60758, 431421, 1572, 2663002, 1…
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 76.1, 67.3, 86.9, 64.2, 79.8, 7…
## $ Additional_Doses_Moderna               <dbl> 93205, 462199, 10843, 2599915, …
## $ Additional_Doses_Pfizer                <dbl> 118869, 566485, 1155, 3440935, …
## $ Additional_Doses_Janssen               <dbl> 3109, 13884, 2, 108984, 1777, 6…
## $ Additional_Doses_Unk_Manuf             <dbl> 66, 110, 0, 9607, 6, 22, 148, 4…
## $ Second_Booster                         <dbl> NA, NA, NA, NA, NA, NA, NA, 217…
## $ Second_Booster_50Plus                  <dbl> 38932, 172409, 1099, 1260797, 1…
## $ Second_Booster_50Plus_Vax_Pct          <dbl> 32.6, 23.6, 22.9, 29.0, 31.0, 1…
## $ Second_Booster_65Plus                  <dbl> 23911, 122667, 374, 920552, 546…
## $ Second_Booster_65Plus_Vax_Pct          <dbl> 39.4, 28.4, 23.8, 34.6, 40.9, 2…
## $ Second_Booster_Janssen                 <dbl> 54, 154, 0, 1932, 5, 3, 778, 19…
## $ Second_Booster_Moderna                 <dbl> 22049, 82271, 1121, 661693, 471…
## $ Second_Booster_Pfizer                  <dbl> 19472, 99685, 20, 657146, 6392,…
## $ Second_Booster_Unk_Manuf               <dbl> 14, 9, 0, 3661, 0, 2, 36, 14924…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
##   isType tot_cases tot_deaths     new_cases    new_deaths         n
##   <chr>      <dbl>      <dbl>         <dbl>         <dbl>     <dbl>
## 1 before  3.23e+10    4.82e+8 91032457      1010326       54575    
## 2 after   3.20e+10    4.80e+8 90063255      1004965       47175    
## 3 pctchg  6.40e- 3    4.51e-3        0.0106       0.00531     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 47,175
## Columns: 6
## $ date       <date> 2022-01-14, 2020-07-11, 2020-02-04, 2022-05-30, 2020-06-22…
## $ state      <chr> "KS", "TN", "AR", "AK", "PA", "TX", "SD", "IN", "HI", "OH",…
## $ tot_cases  <dbl> 621273, 59582, 0, 251425, 86552, 361125, 122688, 5, 661, 10…
## $ tot_deaths <dbl> 7162, 723, 0, 1252, 6426, 7981, 1967, 0, 17, 18646, 17818, …
## $ new_cases  <dbl> 19414, 1964, 0, 0, 459, 9507, 28, 2, 8, 2293, 451, 69, 1, 0…
## $ new_deaths <dbl> 21, 13, 0, 0, 0, 281, 0, 0, 0, 0, 5, 0, 1, 0, 0, 121, 30, 3…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 4.98e+7    4.32e+7 1171904      47585     
## 2 after  4.96e+7    4.30e+7 1148807      45498     
## 3 pctchg 5.27e-3    5.04e-3       0.0197     0.0439
## 
## 
## Processed for cdcHosp:
## Rows: 45,498
## Columns: 5
## $ date       <date> 2020-10-16, 2020-10-10, 2020-10-09, 2020-10-05, 2020-10-01…
## $ state      <chr> "VT", "AL", "VT", "AK", "VT", "RI", "VT", "VT", "RI", "RI",…
## $ inp        <dbl> 2, 983, 0, 44, 1, 91, 3, 1, 85, 78, 50, 3, 36, 22, 191, 69,…
## $ hosp_adult <dbl> 1, 961, 0, 43, 1, 90, 2, 1, 84, 78, 49, 3, 35, 22, 189, 66,…
## $ hosp_ped   <dbl> 1, 22, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 185, 0,…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…²       n
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
## 1 before 4.09e+11 1.68e+11 1495372.    4.27e+10 2.22e+6 1.56e+11 1.76e+6 3.59e+4
## 2 after  1.97e+11 8.13e+10 1251882.    2.07e+10 1.96e+6 7.53e+10 1.49e+6 2.84e+4
## 3 pctchg 5.18e- 1 5.16e- 1       0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
## 
## 
## Processed for vax:
## Rows: 28,407
## Columns: 9
## $ date        <date> 2022-08-03, 2022-08-03, 2022-08-03, 2022-08-03, 2022-08-0…
## $ state       <chr> "AK", "LA", "FL", "NJ", "SC", "MT", "OH", "GA", "WY", "MI"…
## $ vxa         <dbl> 1193878, 6490846, 39035508, 17969587, 7864789, 1658980, 18…
## $ vxc         <dbl> 461227, 2517249, 14592869, 6823997, 2987802, 615118, 69054…
## $ vxcpoppct   <dbl> 63.0, 54.1, 67.9, 76.8, 58.0, 57.6, 59.1, 55.7, 51.7, 60.8…
## $ vxcgte65    <dbl> 79796, 641441, 4150448, 1395646, 834250, 179844, 1805787, …
## $ vxcgte65pct <dbl> 87.1, 86.6, 92.3, 94.6, 89.0, 87.1, 88.2, 84.6, 85.2, 89.2…
## $ vxcgte18    <dbl> 406880, 2316905, 13422863, 6036240, 2728794, 560758, 62552…
## $ vxcgte18pct <dbl> 73.8, 65.1, 77.8, 86.9, 67.6, 66.7, 68.7, 66.0, 61.7, 70.0…
## 
## Integrated per capita data file:
## Rows: 47,388
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition

saveToRDS(cdc_daily_220805, ovrWriteError=FALSE)
## 
## File already exists: ./RInputFiles/Coronavirus/cdc_daily_220805.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE
## NULL
# Run for latest data, save as RDS
indivHosp_20220805 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220805.csv")
## 
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220805.csv already exists
## File will not be downloaded since ovrWrite is not TRUE
## Rows: 235862 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl  (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl    (2): is_metro_micro, is_corrected
## date   (1): collection_week
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 235,862
## Columns: 128
## $ hospital_pk                                                                        <chr> …
## $ collection_week                                                                    <date> …
## $ state                                                                              <chr> …
## $ ccn                                                                                <chr> …
## $ hospital_name                                                                      <chr> …
## $ address                                                                            <chr> …
## $ city                                                                               <chr> …
## $ zip                                                                                <chr> …
## $ hospital_subtype                                                                   <chr> …
## $ fips_code                                                                          <chr> …
## $ is_metro_micro                                                                     <lgl> …
## $ total_beds_7_day_avg                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> …
## $ inpatient_beds_used_7_day_avg                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> …
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> …
## $ inpatient_beds_7_day_avg                                                           <dbl> …
## $ total_icu_beds_7_day_avg                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> …
## $ icu_beds_used_7_day_avg                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> …
## $ total_beds_7_day_sum                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> …
## $ inpatient_beds_used_7_day_sum                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> …
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> …
## $ inpatient_beds_7_day_sum                                                           <dbl> …
## $ total_icu_beds_7_day_sum                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> …
## $ icu_beds_used_7_day_sum                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> …
## $ total_beds_7_day_coverage                                                          <dbl> …
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> …
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> …
## $ inpatient_beds_7_day_coverage                                                      <dbl> …
## $ total_icu_beds_7_day_coverage                                                      <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> …
## $ icu_beds_used_7_day_coverage                                                       <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> …
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> …
## $ geocoded_hospital_address                                                          <chr> …
## $ hhs_ids                                                                            <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> …
## $ is_corrected                                                                       <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> …
## 
## Hospital Subtype Counts:
## # A tibble: 4 × 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         4440
## 2 Critical Access Hospitals  63222
## 3 Long Term                  16209
## 4 Short Term                151991
## 
## Records other than 50 states and DC
## # A tibble: 5 × 2
##   state     n
##   <chr> <int>
## 1 AS       48
## 2 GU       94
## 3 MP       42
## 4 PR     2539
## 5 VI       94
## 
## Record types for key metrics
## # A tibble: 8 × 5
##   name                                               `NA` Posit…¹ Value…²  Total
##   <chr>                                             <int>   <int>   <int>  <int>
## 1 all_adult_hospital_beds_7_day_avg                 54898  180522     442 235862
## 2 all_adult_hospital_inpatient_bed_occupied_7_day_…   119  216617   19126 235862
## 3 icu_beds_used_7_day_avg                              54  207812   27996 235862
## 4 inpatient_beds_7_day_avg                             65  234891     906 235862
## 5 staffed_icu_adult_patients_confirmed_and_suspect…   150  162689   73023 235862
## 6 total_adult_patients_hospitalized_confirmed_and_…   112  159480   76270 235862
## 7 total_beds_7_day_avg                              53263  182340     259 235862
## 8 total_icu_beds_7_day_avg                             61  223623   12178 235862
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220805, ovrWriteError=FALSE)
## 
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220805.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE
## NULL

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220805 <- postProcessCDCDaily(cdc_daily_220805, 
                                              dataThruLabel="Jul 2022", 
                                              keyDatesBurden=c("2022-07-31", "2022-01-31", 
                                                               "2021-07-31", "2021-01-31"
                                                               ),
                                              keyDatesVaccine=c("2022-07-27", "2022-03-31", 
                                                                "2021-11-30", "2021-07-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220805 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220805, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220805$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 × 6
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con… 4.54e4 47585
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 2.75e5 47585
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 3.99e5 47585
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 4.84e5 47585
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 7.73e5 47585
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 1.00e6 47585
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 1.00e6 47585
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 8.87e5 47585
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus… 3.72e4 47585
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 2.48e5 47585
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 3.26e5 47585
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 3.30e5 47585
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 5.22e5 47585
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 7.17e5 47585
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 6.97e5 47585
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 6.35e5 47585
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 1.56e5 47585
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid… 3.58e5 47585

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220805)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 7,344 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 7,334 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity plots are also updated:

modStateHosp_20220805 <- hospitalCapacityCDCDaily(indivHosp_20220805, plotSub="August 2020 to July 2022")
## Warning: Removed 4 row(s) containing missing values (geom_path).

## Warning: Removed 4 row(s) containing missing values (geom_path).

Hospital data appear to be anomalous prior to mid-2021. Record counts are compared:

dfTemp <- indivHosp_20220805 %>%
    count(state, collection_week) %>%
    bind_rows(count(readFromRDS("indivHosp_20220704"), state, collection_week), .id="source") %>%
    mutate(source=c("1"="Aug 2022", "2"="Jul 2022")[source]) %>%
    pivot_wider(c(state, collection_week), names_from="source", values_from="n") %>%
    mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), 0, x))) %>%
    pivot_longer(-c(state, collection_week), names_to="source", values_to="n")
dfTemp
## # A tibble: 11,528 × 4
##    state collection_week source       n
##    <chr> <date>          <chr>    <dbl>
##  1 AK    2020-08-07      Aug 2022     1
##  2 AK    2020-08-07      Jul 2022    16
##  3 AK    2020-09-11      Aug 2022     1
##  4 AK    2020-09-11      Jul 2022    16
##  5 AK    2021-08-27      Aug 2022    14
##  6 AK    2021-08-27      Jul 2022    14
##  7 AK    2021-09-03      Aug 2022    14
##  8 AK    2021-09-03      Jul 2022    14
##  9 AK    2021-09-10      Aug 2022    13
## 10 AK    2021-09-10      Jul 2022    14
## # … with 11,518 more rows
## # ℹ Use `print(n = ...)` to see more rows
dfTemp %>%
    ggplot(aes(x=collection_week, y=n)) + 
    geom_line(aes(group=source, color=source)) + 
    facet_wrap(~state, scales="free_y") + 
    labs(title="Number of hospitals with CDC-reported hospital records", 
         subtitle="August 2020 to July 2022",
         x=NULL, 
         y="Hospitals reporting in CDC data"
         ) + 
    scale_color_discrete("Data pulled in:")

indivHosp_20220805 %>%
    skinnyHHS() %>%
    group_by(state, collection_week) %>%
    summarize(across(where(is.numeric), 
                     .fns=function(x) sum(ifelse(is.na(x), 0, ifelse(x==-999999, 0, x)))
                     ), 
              .groups="drop"
              ) %>%
    full_join(tibble::tibble(collection_week=rep(seq.Date(as.Date("2020-07-31"), as.Date("2022-07-24"), by=7), 51), 
                             state=rep(c(state.abb, "DC"), each=104)
                             ), 
              by=c("collection_week", "state")
              ) %>%
    pivot_longer(-c(state, collection_week)) %>%
    filter(str_detect(name, "icu")) %>%
    ggplot(aes(x=collection_week, y=ifelse(is.na(value), 0, value))) + 
    geom_line(aes(group=name, color=name)) + 
    facet_wrap(~state, scales="free_y") + 
    labs(title="Aug 2022 ICU data", x=NULL, y="Sum of ICU Beds") + 
    scale_color_discrete("Metric")